Vehicle autonomous collision prediction and escaping system (ACE)

ABSTRACT

Embodiments herein relate to an autonomous vehicle or self-driving vehicle. The system can determine a collision avoidance path by: 1) predicting the behavior/trajectory of other moving objects (and identifying stationary objects); 2) given the driving trajectory (issued by autonomous driving system) or predicted driving trajectory (human), establishing the probability for a collision that can be calculated between the vehicle and one or more objects; and 3) finding a path to minimize the collision probability.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims the benefits of and priority, under 35 U.S.C. § 119(e), to U.S. Provisional Application Ser. No. 62/424,976, filed on Nov. 21, 2016, entitled “Next Generation Vehicle,” the entire disclosure of which is hereby incorporated by reference, in its entirety, for all that it teaches and for all purposes.

FIELD

The present disclosure is generally directed to vehicle systems, in particular, toward collision avoidance systems.

BACKGROUND

In recent years, transportation methods have changed substantially. With human error responsible for most accidents, automobile manufactures have been pursuing technologies to assist drivers avoid accidents. Generally, these systems can determine that an object in front of the vehicle is slowing or obstructing the path of the vehicle. In these circumstances, the vehicle can automatically brake without user input. However, current systems cannot generally monitor numerous objects and adjust the path of the vehicle based on the behavior of the objects.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a vehicle in accordance with embodiments of the present disclosure;

FIG. 2 shows a plan view of the vehicle in accordance with at least some embodiments of the present disclosure;

FIG. 3A is a block diagram of an embodiment of a communication environment of the vehicle in accordance with embodiments of the present disclosure;

FIG. 3B is a block diagram of an embodiment of interior sensors within the vehicle in accordance with embodiments of the present disclosure;

FIG. 3C is a block diagram of an embodiment of a navigation system of the vehicle in accordance with embodiments of the present disclosure;

FIG. 4 shows an embodiment of the instrument panel of the vehicle according to one embodiment of the present disclosure;

FIG. 5 is a block diagram of an embodiment of a communications subsystem of the vehicle;

FIG. 6 is a block diagram of a computing environment associated with the embodiments presented herein;

FIG. 7 is a block diagram of a computing device associated with one or more components described herein;

FIG. 8A is a data diagram of sensor readings associated with an object in the driving environment according to embodiments of the present disclosure;

FIG. 8B is a data diagram of constraints associated with an object or with the vehicle according to embodiments of the present disclosure;

FIG. 9A is a conceptualization of a model associated with an object's vector according to embodiments of the present disclosure;

FIG. 9B is a conceptualization of a model associated with another object's vector according to embodiments of the present disclosure;

FIG. 10 is a conceptualization of models associated with two or more objects' vectors according to embodiments of the present disclosure;

FIG. 11 is a conceptualization of determined escape vectors associated a vehicle according to embodiments of the present disclosure;

FIG. 12 is a block diagram of a computing system associated with one or more components described herein;

FIG. 13 is a tree diagram associated with an analysis process associated with one or more components described herein;

FIG. 14 is a flow diagram of an embodiment of a method for avoiding a collision;

FIG. 15 is another flow diagram of an embodiment of a method for avoiding a collision;

FIG. 16 is another flow diagram of an embodiment of a method for avoiding a collision;

FIG. 17 is another flow diagram of an embodiment of a method for avoiding a collision; and

FIG. 18 is an illustration of a driving environment.

DETAILED DESCRIPTION

Embodiments of the present disclosure will be described in connection with a vehicle, and in some embodiments, an electric vehicle, rechargeable electric vehicle, and/or hybrid-electric vehicle and associated systems. Some embodiments relate to an autonomous vehicle or self-driving vehicle. The systems and methods described herein can be applied to cars/airplanes/spaceships for collision prediction/escaping. The system can contain the following three parts: 1) predicting the behavior/trajectory of other moving objects (and identifying stationary objects); 2) given the driving trajectory (issued by autonomous driving system) or predicted driving trajectory (human), the probability for a collision can be calculated between the vehicle and one or more objects; and 3) find a path to minimize the collision probability.

For many objects, the trajectory can be predicted by the laws of physics. For cars, pedestrians, bicycles, or other human-controlled conveyances the distribution of possible trajectories can be predicted by a machine learning model based on data, which can include information associated with object recognition, the road environment, road lanes, traffic signs, etc. With the above, a function can calculate and/or estimate the probability the vehicle will collide with any of the other objects at time step tin the future given the vehicle's current and/or future position.

For moving objects with estimated trajectories, algorithms are designed to discretize the trajectory distribution of the object(s) and record the object trajectory(ies) with an associated probability. The vehicle can then check against all the possible trajectories of all objects to estimate the probability of collision for each step in time. The probability for the collision can be calculated by determining whether an object and the vehicle will occupy a same position at some time step t.

Then, the vehicle can run a Monte Carlo simulation based on the trajectory distribution of objects along with a Monte Carlo tree search and check the collision probability against any simulated trajectory instead. The vehicle attempts to find the path to minimize the collision probability based on a Monte Carlo Tree Search algorithm. Each child node in the tree search can be weighted and expanded. The child may be randomly selected for expansion with the probability proportional to the weight of the child. At the last level of the tree structure, the vehicle can evaluate the leaf nodes. The leaf node collision probability which including estimation of collision probability for the future steps can be estimated by machine learning models. The product of all non-collision probability of all nodes on the path from the root to the leaf will be the non-collision probability for the path or safety score of the path. (safety_score_path=(1−p1)*(1−p2)* . . . (1−pn), where p1, pn are collision probability for each node on the path) The path with the highest safety score can represent the safest path for the vehicle.

Embodiments of the present disclosure will be described in connection with a vehicle, and in some embodiments, an electric vehicle, rechargeable electric vehicle, and/or hybrid-electric vehicle and associated systems.

FIG. 1 shows a perspective view of a vehicle 100 in accordance with embodiments of the present disclosure. The electric vehicle 100 comprises a vehicle front 110, vehicle aft or rear 120, vehicle roof 130, at least one vehicle side 160, a vehicle undercarriage 140, and a vehicle interior 150. In any event, the vehicle 100 may include a frame 104 and one or more body panels 108 mounted or affixed thereto. The vehicle 100 may include one or more interior components (e.g., components inside an interior space 150, or user space, of a vehicle 100, etc.), exterior components (e.g., components outside of the interior space 150, or user space, of a vehicle 100, etc.), drive systems, controls systems, structural components, etc.

Although shown in the form of a car, it should be appreciated that the vehicle 100 described herein may include any conveyance or model of a conveyance, where the conveyance was designed for the purpose of moving one or more tangible objects, such as people, animals, cargo, and the like. The term “vehicle” does not require that a conveyance moves or is capable of movement. Typical vehicles may include but are in no way limited to cars, trucks, motorcycles, busses, automobiles, trains, railed conveyances, boats, ships, marine conveyances, submarine conveyances, airplanes, space craft, flying machines, human-powered conveyances, and the like.

In some embodiments, the vehicle 100 may include a number of sensors, devices, and/or systems that are capable of assisting in driving operations. Examples of the various sensors and systems may include, but are in no way limited to, one or more of cameras (e.g., independent, stereo, combined image, etc.), infrared (IR) sensors, radio frequency (RF) sensors, ultrasonic sensors (e.g., transducers, transceivers, etc.), RADAR sensors (e.g., object-detection sensors and/or systems), LIDAR systems, odometry sensors and/or devices (e.g., encoders, etc.), orientation sensors (e.g., accelerometers, gyroscopes, magnetometer, etc.), navigation sensors and systems (e.g., GPS, etc.), and other ranging, imaging, and/or object-detecting sensors. The sensors may be disposed in an interior space 150 of the vehicle 100 and/or on an outside of the vehicle 100. In some embodiments, the sensors and systems may be disposed in one or more portions of a vehicle 100 (e.g., the frame 104, a body panel, a compartment, etc.).

The vehicle sensors and systems may be selected and/or configured to suit a level of operation associated with the vehicle 100. Among other things, the number of sensors used in a system may be altered to increase or decrease information available to a vehicle control system (e.g., affecting control capabilities of the vehicle 100). Additionally or alternatively, the sensors and systems may be part of one or more advanced driver assistance systems (ADAS) associated with a vehicle 100. In any event, the sensors and systems may be used to provide driving assistance at any level of operation (e.g., from fully-manual to fully-autonomous operations, etc.) as described herein.

The various levels of vehicle control and/or operation can be described as corresponding to a level of autonomy associated with a vehicle 100 for vehicle driving operations. For instance, at Level 0, or fully-manual driving operations, a driver (e.g., a human driver) may be responsible for all the driving control operations (e.g., steering, accelerating, braking, etc.) associated with the vehicle. Level 0 may be referred to as a “No Automation” level. At Level 1, the vehicle may be responsible for a limited number of the driving operations associated with the vehicle, while the driver is still responsible for most driving control operations. An example of a Level 1 vehicle may include a vehicle in which the throttle control and/or braking operations may be controlled by the vehicle (e.g., cruise control operations, etc.). Level 1 may be referred to as a “Driver Assistance” level. At Level 2, the vehicle may collect information (e.g., via one or more driving assistance systems, sensors, etc.) about an environment of the vehicle (e.g., surrounding area, roadway, traffic, ambient conditions, etc.) and use the collected information to control driving operations (e.g., steering, accelerating, braking, etc.) associated with the vehicle. In a Level 2 autonomous vehicle, the driver may be required to perform other aspects of driving operations not controlled by the vehicle. Level 2 may be referred to as a “Partial Automation” level. It should be appreciated that Levels 0-2 all involve the driver monitoring the driving operations of the vehicle.

At Level 3, the driver may be separated from controlling all the driving operations of the vehicle except when the vehicle makes a request for the operator to act or intervene in controlling one or more driving operations. In other words, the driver may be separated from controlling the vehicle unless the driver is required to take over for the vehicle. Level 3 may be referred to as a “Conditional Automation” level. At Level 4, the driver may be separated from controlling all the driving operations of the vehicle and the vehicle may control driving operations even when a user fails to respond to a request to intervene. Level 4 may be referred to as a “High Automation” level. At Level 5, the vehicle can control all the driving operations associated with the vehicle in all driving modes. The vehicle in Level 5 may continually monitor traffic, vehicular, roadway, and/or environmental conditions while driving the vehicle. In Level 5, there is no human driver interaction required in any driving mode. Accordingly, Level 5 may be referred to as a “Full Automation” level. It should be appreciated that in Levels 3-5 the vehicle, and/or one or more automated driving systems associated with the vehicle, monitors the driving operations of the vehicle and the driving environment.

As shown in FIG. 1, the vehicle 100 may, for example, include at least one of a ranging and imaging system 112 (e.g., LIDAR, etc.), an imaging sensor 116A, 116F (e.g., camera, IR, etc.), a radio object-detection and ranging system sensors 116B (e.g., RADAR, RF, etc.), ultrasonic sensors 116C, and/or other object-detection sensors 116D, 116E. In some embodiments, the LIDAR system 112 and/or sensors may be mounted on a roof 130 of the vehicle 100. In one embodiment, the RADAR sensors 116B may be disposed at least at a front 110, aft 120, or side 160 of the vehicle 100. Among other things, the RADAR sensors may be used to monitor and/or detect a position of other vehicles, pedestrians, and/or other objects near, or proximal to, the vehicle 100. While shown associated with one or more areas of a vehicle 100, it should be appreciated that any of the sensors and systems 116A-K, 112 illustrated in FIGS. 1 and 2 may be disposed in, on, and/or about the vehicle 100 in any position, area, and/or zone of the vehicle 100.

Referring now to FIG. 2, a plan view of a vehicle 100 will be described in accordance with embodiments of the present disclosure. In particular, FIG. 2 shows a vehicle sensing environment 200 at least partially defined by the sensors and systems 116A-K, 112 disposed in, on, and/or about the vehicle 100. Each sensor 116A-K may include an operational detection range R and operational detection angle. The operational detection range R may define the effective detection limit, or distance, of the sensor 116A-K. In some cases, this effective detection limit may be defined as a distance from a portion of the sensor 116A-K (e.g., a lens, sensing surface, etc.) to a point in space offset from the sensor 116A-K. The effective detection limit may define a distance, beyond which, the sensing capabilities of the sensor 116A-K deteriorate, fail to work, or are unreliable. In some embodiments, the effective detection limit may define a distance, within which, the sensing capabilities of the sensors 116A-K are able to provide accurate and/or reliable detection information. The operational detection angle may define at least one angle of a span, or between horizontal and/or vertical limits, of a sensor 116A-K. As can be appreciated, the operational detection limit and the operational detection angle of a sensor 116A-K together may define the effective detection zone 216A-D (e.g., the effective detection area, and/or volume, etc.) of a sensor 116A-K.

In some embodiments, the vehicle 100 may include a ranging and imaging system 112 such as LIDAR, or the like. The ranging and imaging system 112 may be configured to detect visual information in an environment surrounding the vehicle 100. The visual information detected in the environment surrounding the ranging and imaging system 112 may be processed (e.g., via one or more sensor and/or system processors, etc.) to generate a complete 360-degree view of an environment 200 around the vehicle. The ranging and imaging system 112 may be configured to generate changing 360-degree views of the environment 200 in real-time, for instance, as the vehicle 100 drives. In some cases, the ranging and imaging system 112 may have an effective detection limit 204 that is some distance from the center of the vehicle 100 outward over 360 degrees. The effective detection limit 204 of the ranging and imaging system 112 defines a view zone 208 (e.g., an area and/or volume, etc.) surrounding the vehicle 100. Any object falling outside of the view zone 208 is in the undetected zone 212 and would not be detected by the ranging and imaging system 112 of the vehicle 100.

Sensor data and information may be collected by one or more sensors or systems 116A-K, 112 of the vehicle 100 monitoring the vehicle sensing environment 200. This information may be processed (e.g., via a processor, computer-vision system, etc.) to determine targets (e.g., objects, signs, people, markings, roadways, conditions, etc.) inside one or more detection zones 208, 216A-D associated with the vehicle sensing environment 200. In some cases, information from multiple sensors 116A-K may be processed to form composite sensor detection information. For example, a first sensor 116A and a second sensor 116F may correspond to a first camera 116A and a second camera 116F aimed in a forward traveling direction of the vehicle 100. In this example, images collected by the cameras 116A, 116F may be combined to form stereo image information. This composite information may increase the capabilities of a single sensor in the one or more sensors 116A-K by, for example, adding the ability to determine depth associated with targets in the one or more detection zones 208, 216A-D. Similar image data may be collected by rear view cameras (e.g., sensors 116G, 116H) aimed in a rearward traveling direction vehicle 100.

In some embodiments, multiple sensors 116A-K may be effectively joined to increase a sensing zone and provide increased sensing coverage. For instance, multiple RADAR sensors 116B disposed on the front 110 of the vehicle may be joined to provide a zone 216B of coverage that spans across an entirety of the front 110 of the vehicle. In some cases, the multiple RADAR sensors 116B may cover a detection zone 216B that includes one or more other sensor detection zones 216A. These overlapping detection zones may provide redundant sensing, enhanced sensing, and/or provide greater detail in sensing within a particular portion (e.g., zone 216A) of a larger zone (e.g., zone 216B). Additionally or alternatively, the sensors 116A-K of the vehicle 100 may be arranged to create a complete coverage, via one or more sensing zones 208, 216A-D around the vehicle 100. In some areas, the sensing zones 216C of two or more sensors 116D, 116E may intersect at an overlap zone 220. In some areas, the angle and/or detection limit of two or more sensing zones 216C, 216D (e.g., of two or more sensors 116E, 116J, 116K) may meet at a virtual intersection point 224.

The vehicle 100 may include a number of sensors 116E, 116G, 116H, 116J, 116K disposed proximal to the rear 120 of the vehicle 100. These sensors can include, but are in no way limited to, an imaging sensor, camera, IR, a radio object-detection and ranging sensors, RADAR, RF, ultrasonic sensors, and/or other object-detection sensors. Among other things, these sensors 116E, 116G, 116H, 116J, 116K may detect targets near or approaching the rear of the vehicle 100. For example, another vehicle approaching the rear 120 of the vehicle 100 may be detected by one or more of the ranging and imaging system (e.g., LIDAR) 112, rear-view cameras 116G, 116H, and/or rear facing RADAR sensors 116J, 116K. As described above, the images from the rear-view cameras 116G, 116H may be processed to generate a stereo view (e.g., providing depth associated with an object or environment, etc.) for targets visible to both cameras 116G, 116H. As another example, the vehicle 100 may be driving and one or more of the ranging and imaging system 112, front-facing cameras 116A, 116F, front-facing RADAR sensors 116B, and/or ultrasonic sensors 116C may detect targets in front of the vehicle 100. This approach may provide critical sensor information to a vehicle control system in at least one of the autonomous driving levels described above. For instance, when the vehicle 100 is driving autonomously (e.g., Level 3, Level 4, or Level 5) and detects other vehicles stopped in a travel path, the sensor detection information may be sent to the vehicle control system of the vehicle 100 to control a driving operation (e.g., braking, decelerating, etc.) associated with the vehicle 100 (in this example, slowing the vehicle 100 as to avoid colliding with the stopped other vehicles). As yet another example, the vehicle 100 may be operating and one or more of the ranging and imaging system 112, and/or the side-facing sensors 116D, 116E (e.g., RADAR, ultrasonic, camera, combinations thereof, and/or other type of sensor), may detect targets at a side of the vehicle 100. It should be appreciated that the sensors 116A-K may detect a target that is both at a side 160 and a front 110 of the vehicle 100 (e.g., disposed at a diagonal angle to a centerline of the vehicle 100 running from the front 110 of the vehicle 100 to the rear 120 of the vehicle). Additionally or alternatively, the sensors 116A-K may detect a target that is both, or simultaneously, at a side 160 and a rear 120 of the vehicle 100 (e.g., disposed at a diagonal angle to the centerline of the vehicle 100).

FIG. 3 is a is a block diagram of an embodiment of a communication environment 300 of the vehicle 100 in accordance with embodiments of the present disclosure. The communication system 300 may include one or more vehicle driving vehicle sensors and systems 304, sensor processors 340, sensor data memory 344, vehicle control system 348, communications subsystem 350, control data 364, computing devices 368, display devices 372, and other components 374 that may be associated with a vehicle 100. These associated components may be electrically and/or communicatively coupled to one another via at least one bus 360. In some embodiments, the one or more associated components may send and/or receive signals across a communication network 352 to at least one of a navigation source 356A, a control source 356B, or some other entity 356N.

In accordance with at least some embodiments of the present disclosure, the communication network 352 may comprise any type of known communication medium or collection of communication media and may use any type of protocols, such as SIP, TCP/IP, SNA, IPX, AppleTalk, and the like, to transport messages between endpoints. The communication network 352 may include wired and/or wireless communication technologies. The Internet is an example of the communication network 352 that constitutes an Internet Protocol (IP) network consisting of many computers, computing networks, and other communication devices located all over the world, which are connected through many telephone systems and other means. Other examples of the communication network 104 include, without limitation, a standard Plain Old Telephone System (POTS), an Integrated Services Digital Network (ISDN), the Public Switched Telephone Network (PSTN), a Local Area Network (LAN), such as an Ethernet network, a Token-Ring network and/or the like, a Wide Area Network (WAN), a virtual network, including without limitation a virtual private network (“VPN”); the Internet, an intranet, an extranet, a cellular network, an infra-red network; a wireless network (e.g., a network operating under any of the IEEE 802.9 suite of protocols, the Bluetooth® protocol known in the art, and/or any other wireless protocol), and any other type of packet-switched or circuit-switched network known in the art and/or any combination of these and/or other networks. In addition, it can be appreciated that the communication network 352 need not be limited to any one network type, and instead may be comprised of a number of different networks and/or network types. The communication network 352 may comprise a number of different communication media such as coaxial cable, copper cable/wire, fiber-optic cable, antennas for transmitting/receiving wireless messages, and combinations thereof.

The driving vehicle sensors and systems 304 may include at least one navigation 308 (e.g., global positioning system (GPS), etc.), orientation 312, odometry 316, LIDAR 320, RADAR 324, ultrasonic 328, camera 332, infrared (IR) 336, and/or other sensor or system 338. These driving vehicle sensors and systems 304 may be similar, if not identical, to the sensors and systems 116A-K, 112 described in conjunction with FIGS. 1 and 2.

The navigation sensor 308 may include one or more sensors having receivers and antennas that are configured to utilize a satellite-based navigation system including a network of navigation satellites capable of providing geolocation and time information to at least one component of the vehicle 100. Examples of the navigation sensor 308 as described herein may include, but are not limited to, at least one of Garmin® GLO™ family of GPS and GLONASS combination sensors, Garmin® GPS 15×™ family of sensors, Garmin® GPS 16×™ family of sensors with high-sensitivity receiver and antenna, Garmin® GPS 18× OEM family of high-sensitivity GPS sensors, Dewetron DEWE-VGPS series of GPS sensors, GlobalSat 1-Hz series of GPS sensors, other industry-equivalent navigation sensors and/or systems, and may perform navigational and/or geolocation functions using any known or future-developed standard and/or architecture.

The orientation sensor 312 may include one or more sensors configured to determine an orientation of the vehicle 100 relative to at least one reference point. In some embodiments, the orientation sensor 312 may include at least one pressure transducer, stress/strain gauge, accelerometer, gyroscope, and/or geomagnetic sensor. Examples of the navigation sensor 308 as described herein may include, but are not limited to, at least one of Bosch Sensortec BMX 160 series low-power absolute orientation sensors, Bosch Sensortec BMX055 9-axis sensors, Bosch Sensortec BMI055 6-axis inertial sensors, Bosch Sensortec BMI160 6-axis inertial sensors, Bosch Sensortec BMF055 9-axis inertial sensors (accelerometer, gyroscope, and magnetometer) with integrated Cortex M0+ microcontroller, Bosch Sensortec BMP280 absolute barometric pressure sensors, Infineon TLV493D-A1B6 3D magnetic sensors, Infineon TLI493D-W1B6 3D magnetic sensors, Infineon TL family of 3D magnetic sensors, Murata Electronics SCC2000 series combined gyro sensor and accelerometer, Murata Electronics SCC1300 series combined gyro sensor and accelerometer, other industry-equivalent orientation sensors and/or systems, and may perform orientation detection and/or determination functions using any known or future-developed standard and/or architecture.

The odometry sensor and/or system 316 may include one or more components that is configured to determine a change in position of the vehicle 100 over time. In some embodiments, the odometry system 316 may utilize data from one or more other sensors and/or systems 304 in determining a position (e.g., distance, location, etc.) of the vehicle 100 relative to a previously measured position for the vehicle 100. Additionally or alternatively, the odometry sensors 316 may include one or more encoders, Hall speed sensors, and/or other measurement sensors/devices configured to measure a wheel speed, rotation, and/or number of revolutions made over time. Examples of the odometry sensor/system 316 as described herein may include, but are not limited to, at least one of Infineon TLE4924/26/27/28C high-performance speed sensors, Infineon TL4941plusC(B) single chip differential Hall wheel-speed sensors, Infineon TL5041plusC Giant Magnetoresistance (GMR) effect sensors, Infineon TL family of magnetic sensors, EPC Model 25SP Accu-CoderPro™ incremental shaft encoders, EPC Model 30M compact incremental encoders with advanced magnetic sensing and signal processing technology, EPC Model 925 absolute shaft encoders, EPC Model 958 absolute shaft encoders, EPC Model MA36S/MA63S/SA36S absolute shaft encoders, Dynapar™ F18 commutating optical encoder, Dynapar™ HS35R family of phased array encoder sensors, other industry-equivalent odometry sensors and/or systems, and may perform change in position detection and/or determination functions using any known or future-developed standard and/or architecture.

The LIDAR sensor/system 320 may include one or more components configured to measure distances to targets using laser illumination. In some embodiments, the LIDAR sensor/system 320 may provide 3D imaging data of an environment around the vehicle 100. The imaging data may be processed to generate a full 360-degree view of the environment around the vehicle 100. The LIDAR sensor/system 320 may include a laser light generator configured to generate a plurality of target illumination laser beams (e.g., laser light channels). In some embodiments, this plurality of laser beams may be aimed at, or directed to, a rotating reflective surface (e.g., a mirror) and guided outwardly from the LIDAR sensor/system 320 into a measurement environment. The rotating reflective surface may be configured to continually rotate 360 degrees about an axis, such that the plurality of laser beams is directed in a full 360-degree range around the vehicle 100. A photodiode receiver of the LIDAR sensor/system 320 may detect when light from the plurality of laser beams emitted into the measurement environment returns (e.g., reflected echo) to the LIDAR sensor/system 320. The LIDAR sensor/system 320 may calculate, based on a time associated with the emission of light to the detected return of light, a distance from the vehicle 100 to the illuminated target. In some embodiments, the LIDAR sensor/system 320 may generate over 2.0 million points per second and have an effective operational range of at least 100 meters. Examples of the LIDAR sensor/system 320 as described herein may include, but are not limited to, at least one of Velodyne® LiDAR™ HDL-64E 64-channel LIDAR sensors, Velodyne® LiDAR™ HDL-32E 32-channel LIDAR sensors, Velodyne® LiDAR™ PUCK™ VLP-16 16-channel LIDAR sensors, Leica Geosystems Pegasus:Two mobile sensor platform, Garmin® LIDAR-Lite v3 measurement sensor, Quanergy M8 LiDAR sensors, Quanergy S3 solid state LiDAR sensor, LeddarTech® LeddarVU compact solid state fixed-beam LIDAR sensors, other industry-equivalent LIDAR sensors and/or systems, and may perform illuminated target and/or obstacle detection in an environment around the vehicle 100 using any known or future-developed standard and/or architecture.

The RADAR sensors 324 may include one or more radio components that are configured to detect objects/targets in an environment of the vehicle 100. In some embodiments, the RADAR sensors 324 may determine a distance, position, and/or movement vector (e.g., angle, speed, etc.) associated with a target over time. The RADAR sensors 324 may include a transmitter configured to generate and emit electromagnetic waves (e.g., radio, microwaves, etc.) and a receiver configured to detect returned electromagnetic waves. In some embodiments, the RADAR sensors 324 may include at least one processor configured to interpret the returned electromagnetic waves and determine locational properties of targets. Examples of the RADAR sensors 324 as described herein may include, but are not limited to, at least one of Infineon RASIC™ RTN7735PL transmitter and RRN7745PL/46PL receiver sensors, Autoliv ASP Vehicle RADAR sensors, Delphi L2C0051TR 77 GHz ESR Electronically Scanning Radar sensors, Fujitsu Ten Ltd. Automotive Compact 77 GHz 3D Electronic Scan Millimeter Wave Radar sensors, other industry-equivalent RADAR sensors and/or systems, and may perform radio target and/or obstacle detection in an environment around the vehicle 100 using any known or future-developed standard and/or architecture.

The ultrasonic sensors 328 may include one or more components that are configured to detect objects/targets in an environment of the vehicle 100. In some embodiments, the ultrasonic sensors 328 may determine a distance, position, and/or movement vector (e.g., angle, speed, etc.) associated with a target over time. The ultrasonic sensors 328 may include an ultrasonic transmitter and receiver, or transceiver, configured to generate and emit ultrasound waves and interpret returned echoes of those waves. In some embodiments, the ultrasonic sensors 328 may include at least one processor configured to interpret the returned ultrasonic waves and determine locational properties of targets. Examples of the ultrasonic sensors 328 as described herein may include, but are not limited to, at least one of Texas Instruments TIDA-00151 automotive ultrasonic sensor interface IC sensors, MaxBotix® MB8450 ultrasonic proximity sensor, MaxBotix® ParkSonar™-EZ ultrasonic proximity sensors, Murata Electronics MA40H1S-R open-structure ultrasonic sensors, Murata Electronics MA40S4R/S open-structure ultrasonic sensors, Murata Electronics MA58MF14-7N waterproof ultrasonic sensors, other industry-equivalent ultrasonic sensors and/or systems, and may perform ultrasonic target and/or obstacle detection in an environment around the vehicle 100 using any known or future-developed standard and/or architecture.

The camera sensors 332 may include one or more components configured to detect image information associated with an environment of the vehicle 100. In some embodiments, the camera sensors 332 may include a lens, filter, image sensor, and/or a digital image processor. It is an aspect of the present disclosure that multiple camera sensors 332 may be used together to generate stereo images providing depth measurements. Examples of the camera sensors 332 as described herein may include, but are not limited to, at least one of ON Semiconductor® MT9V024 Global Shutter VGA GS CMOS image sensors, Teledyne DALSA Falcon2 camera sensors, CMOSIS CMV50000 high-speed CMOS image sensors, other industry-equivalent camera sensors and/or systems, and may perform visual target and/or obstacle detection in an environment around the vehicle 100 using any known or future-developed standard and/or architecture.

The infrared (IR) sensors 336 may include one or more components configured to detect image information associated with an environment of the vehicle 100. The IR sensors 336 may be configured to detect targets in low-light, dark, or poorly-lit environments. The IR sensors 336 may include an IR light emitting element (e.g., IR light emitting diode (LED), etc.) and an IR photodiode. In some embodiments, the IR photodiode may be configured to detect returned IR light at or about the same wavelength to that emitted by the IR light emitting element. In some embodiments, the IR sensors 336 may include at least one processor configured to interpret the returned IR light and determine locational properties of targets. The IR sensors 336 may be configured to detect and/or measure a temperature associated with a target (e.g., an object, pedestrian, other vehicle, etc.). Examples of IR sensors 336 as described herein may include, but are not limited to, at least one of Opto Diode lead-salt IR array sensors, Opto Diode OD-850 Near-IR LED sensors, Opto Diode SA/SHA727 steady state IR emitters and IR detectors, FLIR® LS microbolometer sensors, FLIR® TacFLIR 380-HD InSb MWIR FPA and HD MWIR thermal sensors, FLIR® VOx 640×480 pixel detector sensors, Delphi IR sensors, other industry-equivalent IR sensors and/or systems, and may perform IR visual target and/or obstacle detection in an environment around the vehicle 100 using any known or future-developed standard and/or architecture.

The vehicle 100 can also include one or more interior sensors 337. Interior sensors 337 can measure characteristics of the inside environment of the vehicle 100. The interior sensors 337 may be as described in conjunction with FIG. 3B.

A navigation system 302 can include any hardware and/or software used to navigate the vehicle either manually or autonomously. The navigation system 302 may be as described in conjunction with FIG. 3C.

In some embodiments, the driving vehicle sensors and systems 304 may include other sensors 338 and/or combinations of the sensors 306-337 described above. Additionally or alternatively, one or more of the sensors 306-337 described above may include one or more processors configured to process and/or interpret signals detected by the one or more sensors 306-337. In some embodiments, the processing of at least some sensor information provided by the vehicle sensors and systems 304 may be processed by at least one sensor processor 340. Raw and/or processed sensor data may be stored in a sensor data memory 344 storage medium. In some embodiments, the sensor data memory 344 may store instructions used by the sensor processor 340 for processing sensor information provided by the sensors and systems 304. In any event, the sensor data memory 344 may be a disk drive, optical storage device, solid-state storage device such as a random access memory (“RAM”) and/or a read-only memory (“ROM”), which can be programmable, flash-updateable, and/or the like.

The vehicle control system 348 may receive processed sensor information from the sensor processor 340 and determine to control an aspect of the vehicle 100. Controlling an aspect of the vehicle 100 may include presenting information via one or more display devices 372 associated with the vehicle, sending commands to one or more computing devices 368 associated with the vehicle, and/or controlling a driving operation of the vehicle. In some embodiments, the vehicle control system 348 may correspond to one or more computing systems that control driving operations of the vehicle 100 in accordance with the Levels of driving autonomy described above. In one embodiment, the vehicle control system 348 may operate a speed of the vehicle 100 by controlling an output signal to the accelerator and/or braking system of the vehicle. In this example, the vehicle control system 348 may receive sensor data describing an environment surrounding the vehicle 100 and, based on the sensor data received, determine to adjust the acceleration, power output, and/or braking of the vehicle 100. The vehicle control system 348 may additionally control steering and/or other driving functions of the vehicle 100.

The vehicle control system 348 may communicate, in real-time, with the driving sensors and systems 304 forming a feedback loop. In particular, upon receiving sensor information describing a condition of targets in the environment surrounding the vehicle 100, the vehicle control system 348 may autonomously make changes to a driving operation of the vehicle 100. The vehicle control system 348 may then receive subsequent sensor information describing any change to the condition of the targets detected in the environment as a result of the changes made to the driving operation. This continual cycle of observation (e.g., via the sensors, etc.) and action (e.g., selected control or non-control of vehicle operations, etc.) allows the vehicle 100 to operate autonomously in the environment.

In some embodiments, the one or more components of the vehicle 100 (e.g., the driving vehicle sensors 304, vehicle control system 348, display devices 372, etc.) may communicate across the communication network 352 to one or more entities 356A-N via a communications subsystem 350 of the vehicle 100. Embodiments of the communications subsystem 350 are described in greater detail in conjunction with FIG. 5. For instance, the navigation sensors 308 may receive global positioning, location, and/or navigational information from a navigation source 356A. In some embodiments, the navigation source 356A may be a global navigation satellite system (GNSS) similar, if not identical, to NAVSTAR GPS, GLONASS, EU Galileo, and/or the BeiDou Navigation Satellite System (BDS) to name a few.

In some embodiments, the vehicle control system 348 may receive control information from one or more control sources 356B. The control source 356 may provide vehicle control information including autonomous driving control commands, vehicle operation override control commands, and the like. The control source 356 may correspond to an autonomous vehicle control system, a traffic control system, an administrative control entity, and/or some other controlling server. It is an aspect of the present disclosure that the vehicle control system 348 and/or other components of the vehicle 100 may exchange communications with the control source 356 across the communication network 352 and via the communications subsystem 350.

Information associated with controlling driving operations of the vehicle 100 may be stored in a control data memory 364 storage medium. The control data memory 364 may store instructions used by the vehicle control system 348 for controlling driving operations of the vehicle 100, historical control information, autonomous driving control rules, and the like. In some embodiments, the control data memory 364 may be a disk drive, optical storage device, solid-state storage device such as a random access memory (“RAM”) and/or a read-only memory (“ROM”), which can be programmable, flash-updateable, and/or the like.

In addition to the mechanical components described herein, the vehicle 100 may include a number of user interface devices. The user interface devices receive and translate human input into a mechanical movement or electrical signal or stimulus. The human input may be one or more of motion (e.g., body movement, body part movement, in two-dimensional or three-dimensional space, etc.), voice, touch, and/or physical interaction with the components of the vehicle 100. In some embodiments, the human input may be configured to control one or more functions of the vehicle 100 and/or systems of the vehicle 100 described herein. User interfaces may include, but are in no way limited to, at least one graphical user interface of a display device, steering wheel or mechanism, transmission lever or button (e.g., including park, neutral, reverse, and/or drive positions, etc.), throttle control pedal or mechanism, brake control pedal or mechanism, power control switch, communications equipment, etc.

FIG. 3B shows a block diagram of an embodiment of interior sensors 337 for a vehicle 100. The interior sensors 337 may be arranged into one or more groups, based at least partially on the function of the interior sensors 337. For example, the interior space of a vehicle 100 may include environmental sensors, a user interface sensors, and/or safety sensors. Additionally or alternatively, there may be sensors associated with various devices inside the vehicle (e.g., smart phones, tablets, mobile computers, wearables, etc.).

Environmental sensors may comprise sensors configured to collect data relating to the internal environment of a vehicle 100. Examples of environmental sensors may include one or more of, but are not limited to: oxygen/air sensors 301, temperature sensors 303, humidity sensors 305, light/photo sensors 307, and more. The oxygen/air sensors 301 may be configured to detect a quality or characteristic of the air in the interior space 108 of the vehicle 100 (e.g., ratios and/or types of gasses comprising the air inside the vehicle 100, dangerous gas levels, safe gas levels, etc.). Temperature sensors 303 may be configured to detect temperature readings of one or more objects, users 216, and/or areas of a vehicle 100. Humidity sensors 305 may detect an amount of water vapor present in the air inside the vehicle 100. The light/photo sensors 307 can detect an amount of light present in the vehicle 100. Further, the light/photo sensors 307 may be configured to detect various levels of light intensity associated with light in the vehicle 100.

User interface sensors may comprise sensors configured to collect data relating to one or more users (e.g., a driver and/or passenger(s)) in a vehicle 100. As can be appreciated, the user interface sensors may include sensors that are configured to collect data from users 216 in one or more areas of the vehicle 100. Examples of user interface sensors may include one or more of, but are not limited to: infrared sensors 309, motion sensors 311, weight sensors 313, wireless network sensors 315, biometric sensors 317, camera (or image) sensors 319, audio sensors 321, and more.

Infrared sensors 309 may be used to measure IR light irradiating from at least one surface, user, or another object in the vehicle 100. Among other things, the Infrared sensors 309 may be used to measure temperatures, form images (especially in low light conditions), identify users 216, and even detect motion in the vehicle 100.

The motion sensors 311 may detect motion and/or movement of objects inside the vehicle 104. Optionally, the motion sensors 311 may be used alone or in combination to detect movement. For example, a user may be operating a vehicle 100 (e.g., while driving, etc.) when a passenger in the rear of the vehicle 100 unbuckles a safety belt and proceeds to move about the vehicle 10. In this example, the movement of the passenger could be detected by the motion sensors 311. In response to detecting the movement and/or the direction associated with the movement, the passenger may be prevented from interfacing with and/or accessing at least some of the vehicle control features. As can be appreciated, the user may be alerted of the movement/motion such that the user can act to prevent the passenger from interfering with the vehicle controls. Optionally, the number of motion sensors in a vehicle may be increased to increase an accuracy associated with motion detected in the vehicle 100.

Weight sensors 313 may be employed to collect data relating to objects and/or users in various areas of the vehicle 100. In some cases, the weight sensors 313 may be included in the seats and/or floor of a vehicle 100. Optionally, the vehicle 100 may include a wireless network sensor 315. This sensor 315 may be configured to detect one or more wireless network(s) inside the vehicle 100. Examples of wireless networks may include, but are not limited to, wireless communications utilizing Bluetooth®, Wi-Fi™, ZigBee, IEEE 802.11, and other wireless technology standards. For example, a mobile hotspot may be detected inside the vehicle 100 via the wireless network sensor 315. In this case, the vehicle 100 may determine to utilize and/or share the mobile hotspot detected via/with one or more other devices associated with the vehicle 100.

Biometric sensors 317 may be employed to identify and/or record characteristics associated with a user. It is anticipated that biometric sensors 317 can include at least one of image sensors, IR sensors, fingerprint readers, weight sensors, load cells, force transducers, heart rate monitors, blood pressure monitors, and the like as provided herein.

The camera sensors 319 may record still images, video, and/or combinations thereof. Camera sensors 319 may be used alone or in combination to identify objects, users, and/or other features, inside the vehicle 100. Two or more camera sensors 319 may be used in combination to form, among other things, stereo and/or three-dimensional (3D) images. The stereo images can be recorded and/or used to determine depth associated with objects and/or users in a vehicle 100. Further, the camera sensors 319 used in combination may determine the complex geometry associated with identifying characteristics of a user. For example, the camera sensors 319 may be used to determine dimensions between various features of a user's face (e.g., the depth/distance from a user's nose to a user's cheeks, a linear distance between the center of a user's eyes, and more). These dimensions may be used to verify, record, and even modify characteristics that serve to identify a user. The camera sensors 319 may also be used to determine movement associated with objects and/or users within the vehicle 100. It should be appreciated that the number of image sensors used in a vehicle 100 may be increased to provide greater dimensional accuracy and/or views of a detected image in the vehicle 100.

The audio sensors 321 may be configured to receive audio input from a user of the vehicle 100. The audio input from a user may correspond to voice commands, conversations detected in the vehicle 100, phone calls made in the vehicle 100, and/or other audible expressions made in the vehicle 100. Audio sensors 321 may include, but are not limited to, microphones and other types of acoustic-to-electric transducers or sensors. Optionally, the interior audio sensors 321 may be configured to receive and convert sound waves into an equivalent analog or digital signal. The interior audio sensors 321 may serve to determine one or more locations associated with various sounds in the vehicle 100. The location of the sounds may be determined based on a comparison of volume levels, intensity, and the like, between sounds detected by two or more interior audio sensors 321. For instance, a first audio sensor 321 may be located in a first area of the vehicle 100 and a second audio sensor 321 may be located in a second area of the vehicle 100. If a sound is detected at a first volume level by the first audio sensors 321 A and a second, higher, volume level by the second audio sensors 321 in the second area of the vehicle 100, the sound may be determined to be closer to the second area of the vehicle 100. As can be appreciated, the number of sound receivers used in a vehicle 100 may be increased (e.g., more than two, etc.) to increase measurement accuracy surrounding sound detection and location, or source, of the sound (e.g., via triangulation, etc.).

The safety sensors may comprise sensors configured to collect data relating to the safety of a user and/or one or more components of a vehicle 100. Examples of safety sensors may include one or more of, but are not limited to: force sensors 325, mechanical motion sensors 327, orientation sensors 329, restraint sensors 331, and more.

The force sensors 325 may include one or more sensors inside the vehicle 100 configured to detect a force observed in the vehicle 100. One example of a force sensor 325 may include a force transducer that converts measured forces (e.g., force, weight, pressure, etc.) into output signals. Mechanical motion sensors 327 may correspond to encoders, accelerometers, damped masses, and the like. Optionally, the mechanical motion sensors 327 may be adapted to measure the force of gravity (i.e., G-force) as observed inside the vehicle 100. Measuring the G-force observed inside a vehicle 100 can provide valuable information related to a vehicle's acceleration, deceleration, collisions, and/or forces that may have been suffered by one or more users in the vehicle 100. Orientation sensors 329 can include accelerometers, gyroscopes, magnetic sensors, and the like that are configured to detect an orientation associated with the vehicle 100.

The restraint sensors 331 may correspond to sensors associated with one or more restraint devices and/or systems in a vehicle 100. Seatbelts and airbags are examples of restraint devices and/or systems. As can be appreciated, the restraint devices and/or systems may be associated with one or more sensors that are configured to detect a state of the device/system. The state may include extension, engagement, retraction, disengagement, deployment, and/or other electrical or mechanical conditions associated with the device/system.

The associated device sensors 323 can include any sensors that are associated with a device in the vehicle 100. As previously stated, typical devices may include smart phones, tablets, laptops, mobile computers, and the like. It is anticipated that the various sensors associated with these devices can be employed by the vehicle control system 348. For example, a typical smart phone can include, an image sensor, an IR sensor, audio sensor, gyroscope, accelerometer, wireless network sensor, fingerprint reader, and more. It is an aspect of the present disclosure that one or more of these associated device sensors 323 may be used by one or more subsystems of the vehicle 100.

FIG. 3C illustrates a GPS/Navigation subsystem(s) 302. The navigation subsystem(s) 302 can be any present or future-built navigation system that may use location data, for example, from the Global Positioning System (GPS), to provide navigation information or control the vehicle 100. The navigation subsystem(s) 302 can include several components, such as, one or more of, but not limited to: a GPS Antenna/receiver 331, a location module 333, a maps database 335, etc. Generally, the several components or modules 331-335 may be hardware, software, firmware, computer readable media, or combinations thereof.

A GPS Antenna/receiver 331 can be any antenna, GPS puck, and/or receiver capable of receiving signals from a GPS satellite or other navigation system. The signals may be demodulated, converted, interpreted, etc. by the GPS Antenna/receiver 331 and provided to the location module 333. Thus, the GPS Antenna/receiver 331 may convert the time signals from the GPS system and provide a location (e.g., coordinates on a map) to the location module 333. Alternatively, the location module 333 can interpret the time signals into coordinates or other location information.

The location module 333 can be the controller of the satellite navigation system designed for use in the vehicle 100. The location module 333 can acquire position data, as from the GPS Antenna/receiver 331, to locate the user or vehicle 100 on a road in the unit's map database 335. Using the road database 335, the location module 333 can give directions to other locations along roads also in the database 335. When a GPS signal is not available, the location module 333 may apply dead reckoning to estimate distance data from sensors 304 including one or more of, but not limited to, a speed sensor attached to the drive train of the vehicle 100, a gyroscope, an accelerometer, etc. Additionally or alternatively, the location module 333 may use known locations of Wi-Fi hotspots, cell tower data, etc. to determine the position of the vehicle 100, such as by using time difference of arrival (TDOA) and/or frequency difference of arrival (FDOA) techniques.

The maps database 335 can include any hardware and/or software to store information about maps, geographical information system (GIS) information, location information, etc. The maps database 335 can include any data definition or other structure to store the information. Generally, the maps database 335 can include a road database that may include one or more vector maps of areas of interest. Street names, street numbers, house numbers, and other information can be encoded as geographic coordinates so that the user can find some desired destination by street address. Points of interest (waypoints) can also be stored with their geographic coordinates. For example, a point of interest may include speed cameras, fuel stations, public parking, and “parked here” (or “you parked here”) information. The maps database 335 may also include road or street characteristics, for example, speed limits, location of stop lights/stop signs, lane divisions, school locations, etc. The map database contents can be produced or updated by a server connected through a wireless system in communication with the Internet, even as the vehicle 100 is driven along existing streets, yielding an up-to-date map.

FIG. 4 shows one embodiment of the instrument panel 400 of the vehicle 100. The instrument panel 400 of vehicle 100 comprises a steering wheel 410, a vehicle operational display 420 (e.g., configured to present and/or display driving data such as speed, measured air resistance, vehicle information, entertainment information, etc.), one or more auxiliary displays 424 (e.g., configured to present and/or display information segregated from the operational display 420, entertainment applications, movies, music, etc.), a heads-up display 434 (e.g., configured to display any information previously described including, but in no way limited to, guidance information such as route to destination, or obstacle warning information to warn of a potential collision, or some or all primary vehicle operational data such as speed, resistance, etc.), a power management display 428 (e.g., configured to display data corresponding to electric power levels of vehicle 100, reserve power, charging status, etc.), and an input device 432 (e.g., a controller, touchscreen, or other interface device configured to interface with one or more displays in the instrument panel or components of the vehicle 100. The input device 432 may be configured as a joystick, mouse, touchpad, tablet, 3D gesture capture device, etc.). In some embodiments, the input device 432 may be used to manually maneuver a portion of the vehicle 100 into a charging position (e.g., moving a charging plate to a desired separation distance, etc.).

While one or more of displays of instrument panel 400 may be touch-screen displays, it should be appreciated that the vehicle operational display may be a display incapable of receiving touch input. For instance, the operational display 420 that spans across an interior space centerline 404 and across both a first zone 408A and a second zone 408B may be isolated from receiving input from touch, especially from a passenger. In some cases, a display that provides vehicle operation or critical systems information and interface may be restricted from receiving touch input and/or be configured as a non-touch display. This type of configuration can prevent dangerous mistakes in providing touch input where such input may cause an accident or unwanted control.

In some embodiments, one or more displays of the instrument panel 400 may be mobile devices and/or applications residing on a mobile device such as a smart phone. Additionally or alternatively, any of the information described herein may be presented to one or more portions 420A-N of the operational display 420 or other display 424, 428, 434. In one embodiment, one or more displays of the instrument panel 400 may be physically separated or detached from the instrument panel 400. In some cases, a detachable display may remain tethered to the instrument panel.

The portions 420A-N of the operational display 420 may be dynamically reconfigured and/or resized to suit any display of information as described. Additionally or alternatively, the number of portions 420A-N used to visually present information via the operational display 420 may be dynamically increased or decreased as required, and are not limited to the configurations shown.

FIG. 5 illustrates a hardware diagram of communications componentry that can be optionally associated with the vehicle 100 in accordance with embodiments of the present disclosure.

The communications componentry can include one or more wired or wireless devices such as a transceiver(s) and/or modem that allows communications not only between the various systems disclosed herein but also with other devices, such as devices on a network, and/or on a distributed network such as the Internet and/or in the cloud and/or with another vehicle(s).

The communications subsystem 350 can also include inter- and intra-vehicle communications capabilities such as hotspot and/or access point connectivity for any one or more of the vehicle occupants and/or vehicle-to-vehicle communications.

Additionally, and while not specifically illustrated, the communications subsystem 350 can include one or more communications links (that can be wired or wireless) and/or communications busses (managed by the bus manager 574), including one or more of CANbus, OBD-II, ARCINC 429, Byteflight, CAN (Controller Area Network), D2B (Domestic Digital Bus), FlexRay, DC-BUS, IDB-1394, IEBus, I2C, ISO 9141-1/-2, J1708, J1587, J1850, J1939, ISO 11783, Keyword Protocol 2000, LIN (Local Interconnect Network), MOST (Media Oriented Systems Transport), Multifunction Vehicle Bus, SMARTwireX, SPI, VAN (Vehicle Area Network), and the like or in general any communications protocol and/or standard(s).

The various protocols and communications can be communicated one or more of wirelessly and/or over transmission media such as single wire, twisted pair, fiber optic, IEEE 1394, MIL-STD-1553, MIL-STD-1773, power-line communication, or the like. (All of the above standards and protocols are incorporated herein by reference in their entirety).

As discussed, the communications subsystem 350 enables communications between any if the inter-vehicle systems and subsystems as well as communications with non-collocated resources, such as those reachable over a network such as the Internet.

The communications subsystem 350, in addition to well-known componentry (which has been omitted for clarity), includes interconnected elements including one or more of: one or more antennas 504, an interleaver/deinterleaver 508, an analog front end (AFE) 512, memory/storage/cache 516, controller/microprocessor 520, MAC circuitry 522, modulator/demodulator 524, encoder/decoder 528, a plurality of connectivity managers 534, 558, 562, 566, GPU 540, accelerator 544, a multiplexer/demultiplexer 552, transmitter 570, receiver 572 and wireless radio 578 components such as a Wi-Fi PHY/Bluetooth® module 580, a Wi-Fi/BT MAC module 584, transmitter 588 and receiver 592. The various elements in the device 350 are connected by one or more links/busses 5 (not shown, again for sake of clarity).

The device 350 can have one more antennas 504, for use in wireless communications such as multi-input multi-output (MIMO) communications, multi-user multi-input multi-output (MU-MIMO) communications Bluetooth®, LTE, 4G, 5G, Near-Field Communication (NFC), etc., and in general for any type of wireless communications. The antenna(s) 504 can include, but are not limited to one or more of directional antennas, omnidirectional antennas, monopoles, patch antennas, loop antennas, microstrip antennas, dipoles, and any other antenna(s) suitable for communication transmission/reception. In an exemplary embodiment, transmission/reception using MIMO may require particular antenna spacing. In another exemplary embodiment, MIMO transmission/reception can enable spatial diversity allowing for different channel characteristics at each of the antennas. In yet another embodiment, MIMO transmission/reception can be used to distribute resources to multiple users for example within the vehicle 100 and/or in another vehicle.

Antenna(s) 504 generally interact with the Analog Front End (AFE) 512, which is needed to enable the correct processing of the received modulated signal and signal conditioning for a transmitted signal. The AFE 512 can be functionally located between the antenna and a digital baseband system to convert the analog signal into a digital signal for processing and vice-versa.

The subsystem 350 can also include a controller/microprocessor 520 and a memory/storage/cache 516. The subsystem 350 can interact with the memory/storage/cache 516 which may store information and operations necessary for configuring and transmitting or receiving the information described herein. The memory/storage/cache 516 may also be used in connection with the execution of application programming or instructions by the controller/microprocessor 520, and for temporary or long term storage of program instructions and/or data. As examples, the memory/storage/cache 520 may comprise a computer-readable device, RAM, ROM, DRAM, SDRAM, and/or other storage device(s) and media.

The controller/microprocessor 520 may comprise a general purpose programmable processor or controller for executing application programming or instructions related to the subsystem 350. Furthermore, the controller/microprocessor 520 can perform operations for configuring and transmitting/receiving information as described herein. The controller/microprocessor 520 may include multiple processor cores, and/or implement multiple virtual processors. Optionally, the controller/microprocessor 520 may include multiple physical processors. By way of example, the controller/microprocessor 520 may comprise a specially configured Application Specific Integrated Circuit (ASIC) or other integrated circuit, a digital signal processor(s), a controller, a hardwired electronic or logic circuit, a programmable logic device or gate array, a special purpose computer, or the like.

The subsystem 350 can further include a transmitter 570 and receiver 572 which can transmit and receive signals, respectively, to and from other devices, subsystems and/or other destinations using the one or more antennas 504 and/or links/busses. Included in the subsystem 350 circuitry is the medium access control or MAC Circuitry 522. MAC circuitry 522 provides for controlling access to the wireless medium. In an exemplary embodiment, the MAC circuitry 522 may be arranged to contend for the wireless medium and configure frames or packets for communicating over the wired/wireless medium.

The subsystem 350 can also optionally contain a security module (not shown). This security module can contain information regarding but not limited to, security parameters required to connect the device to one or more other devices or other available network(s), and can include WEP or WPA/WPA-2 (optionally+AES and/or TKIP) security access keys, network keys, etc. The WEP security access key is a security password used by Wi-Fi networks. Knowledge of this code can enable a wireless device to exchange information with an access point and/or another device. The information exchange can occur through encoded messages with the WEP access code often being chosen by the network administrator. WPA is an added security standard that is also used in conjunction with network connectivity with stronger encryption than WEP.

In some embodiments, the communications subsystem 350 also includes a GPU 540, an accelerator 544, a Wi-Fi/BT/BLE PHY module 580 and a Wi-Fi/BT/BLE MAC module 584 and wireless transmitter 588 and receiver 592. In some embodiments, the GPU 540 may be a graphics processing unit, or visual processing unit, comprising at least one circuit and/or chip that manipulates and changes memory to accelerate the creation of images in a frame buffer for output to at least one display device. The GPU 540 may include one or more of a display device connection port, printed circuit board (PCB), a GPU chip, a metal-oxide-semiconductor field-effect transistor (MOSFET), memory (e.g., single data rate random-access memory (SDRAM), double data rate random-access memory (DDR) RAM, etc., and/or combinations thereof), a secondary processing chip (e.g., handling video out capabilities, processing, and/or other functions in addition to the GPU chip, etc.), a capacitor, heatsink, temperature control or cooling fan, motherboard connection, shielding, and the like.

The various connectivity managers 534, 558, 562, 566 manage and/or coordinate communications between the subsystem 350 and one or more of the systems disclosed herein and one or more other devices/systems. The connectivity managers 534, 558, 562, 566 include a charging connectivity manager 534, a vehicle database connectivity manager 558, a remote operating system connectivity manager 562, and a sensor connectivity manager 566.

The charging connectivity manager 534 can coordinate not only the physical connectivity between the vehicle 100 and a charging device/vehicle, but can also communicate with one or more of a power management controller, one or more third parties and optionally a billing system(s). As an example, the vehicle 100 can establish communications with the charging device/vehicle to one or more of coordinate interconnectivity between the two (e.g., by spatially aligning the charging receptacle on the vehicle with the charger on the charging vehicle) and optionally share navigation information. Once charging is complete, the amount of charge provided can be tracked and optionally forwarded to, for example, a third party for billing. In addition to being able to manage connectivity for the exchange of power, the charging connectivity manager 534 can also communicate information, such as billing information to the charging vehicle and/or a third party. This billing information could be, for example, the owner of the vehicle, the driver/occupant(s) of the vehicle, company information, or in general any information usable to charge the appropriate entity for the power received.

The vehicle database connectivity manager 558 allows the subsystem to receive and/or share information stored in the vehicle database. This information can be shared with other vehicle components/subsystems and/or other entities, such as third parties and/or charging systems. The information can also be shared with one or more vehicle occupant devices, such as an app (application) on a mobile device the driver uses to track information about the vehicle 100 and/or a dealer or service/maintenance provider. In general, any information stored in the vehicle database can optionally be shared with any one or more other devices optionally subject to any privacy or confidentially restrictions.

The remote operating system connectivity manager 562 facilitates communications between the vehicle 100 and any one or more autonomous vehicle systems. These communications can include one or more of navigation information, vehicle information, other vehicle information, weather information, occupant information, or in general any information related to the remote operation of the vehicle 100.

The sensor connectivity manager 566 facilitates communications between any one or more of the vehicle sensors (e.g., the driving vehicle sensors and systems 304, etc.) and any one or more of the other vehicle systems. The sensor connectivity manager 566 can also facilitate communications between any one or more of the sensors and/or vehicle systems and any other destination, such as a service company, app, or in general to any destination where sensor data is needed.

In accordance with one exemplary embodiment, any of the communications discussed herein can be communicated via the conductor(s) used for charging. One exemplary protocol usable for these communications is Power-line communication (PLC). PLC is a communication protocol that uses electrical wiring to simultaneously carry both data, and Alternating Current (AC) electric power transmission or electric power distribution. It is also known as power-line carrier, power-line digital subscriber line (PDSL), mains communication, power-line telecommunications, or power-line networking (PLN). For DC environments in vehicles PLC can be used in conjunction with CAN-bus, LIN-bus over power line (DC-LIN) and DC-BUS.

The communications subsystem can also optionally manage one or more identifiers, such as an IP (internet protocol) address(es), associated with the vehicle and one or other system or subsystems or components therein. These identifiers can be used in conjunction with any one or more of the connectivity managers as discussed herein.

FIG. 6 illustrates a block diagram of a computing environment 600 that may function as the servers, user computers, or other systems provided and described herein. The computing environment 600 includes one or more user computers, or computing devices, such as a vehicle computing device 604, a communication device 608, and/or more 612. The computing devices 604, 608, 612 may include general purpose personal computers (including, merely by way of example, personal computers, and/or laptop computers running various versions of Microsoft Corp.'s Windows® and/or Apple Corp.'s Macintosh® operating systems) and/or workstation computers running any of a variety of commercially-available UNIX® or UNIX-like operating systems. These computing devices 604, 608, 612 may also have any of a variety of applications, including for example, database client and/or server applications, and web browser applications. Alternatively, the computing devices 604, 608, 612 may be any other electronic device, such as a thin-client computer, Internet-enabled mobile telephone, and/or personal digital assistant, capable of communicating via a network 352 and/or displaying and navigating web pages or other types of electronic documents. Although the exemplary computing environment 600 is shown with two computing devices, any number of user computers or computing devices may be supported.

The computing environment 600 may also include one or more servers 614, 616. In this example, server 614 is shown as a web server and server 616 is shown as an application server. The web server 614, which may be used to process requests for web pages or other electronic documents from computing devices 604, 608, 612. The web server 614 can be running an operating system including any of those discussed above, as well as any commercially-available server operating systems. The web server 614 can also run a variety of server applications, including SIP (Session Initiation Protocol) servers, HTTP(s) servers, FTP servers, CGI servers, database servers, Java servers, and the like. In some instances, the web server 614 may publish operations available operations as one or more web services.

The computing environment 600 may also include one or more file and or/application servers 616, which can, in addition to an operating system, include one or more applications accessible by a client running on one or more of the computing devices 604, 608, 612. The server(s) 616 and/or 614 may be one or more general purpose computers capable of executing programs or scripts in response to the computing devices 604, 608, 612. As one example, the server 616, 614 may execute one or more web applications. The web application may be implemented as one or more scripts or programs written in any programming language, such as Java™, C, C#®, or C++, and/or any scripting language, such as Perl, Python, or TCL, as well as combinations of any programming/scripting languages. The application server(s) 616 may also include database servers, including without limitation those commercially available from Oracle®, Microsoft®, Sybase®, IBM® and the like, which can process requests from database clients running on a computing device 604, 608, 612.

The web pages created by the server 614 and/or 616 may be forwarded to a computing device 604, 608, 612 via a web (file) server 614, 616. Similarly, the web server 614 may be able to receive web page requests, web services invocations, and/or input data from a computing device 604, 608, 612 (e.g., a user computer, etc.) and can forward the web page requests and/or input data to the web (application) server 616. In further embodiments, the server 616 may function as a file server. Although for ease of description, FIG. 6 illustrates a separate web server 614 and file/application server 616, those skilled in the art will recognize that the functions described with respect to servers 614, 616 may be performed by a single server and/or a plurality of specialized servers, depending on implementation-specific needs and parameters. The computer systems 604, 608, 612, web (file) server 614 and/or web (application) server 616 may function as the system, devices, or components described in FIGS. 1-6.

The computing environment 600 may also include a database 618. The database 618 may reside in a variety of locations. By way of example, database 618 may reside on a storage medium local to (and/or resident in) one or more of the computers 604, 608, 612, 614, 616. Alternatively, it may be remote from any or all of the computers 604, 608, 612, 614, 616, and in communication (e.g., via the network 610) with one or more of these. The database 618 may reside in a storage-area network (“SAN”) familiar to those skilled in the art. Similarly, any necessary files for performing the functions attributed to the computers 604, 608, 612, 614, 616 may be stored locally on the respective computer and/or remotely, as appropriate. The database 618 may be a relational database, such as Oracle 20i®, that is adapted to store, update, and retrieve data in response to SQL-formatted commands.

FIG. 7 illustrates one embodiment of a computer system 700 upon which the servers, user computers, computing devices, or other systems or components described above may be deployed or executed. The computer system 700 is shown comprising hardware elements that may be electrically coupled via a bus 704. The hardware elements may include one or more central processing units (CPUs) 708; one or more input devices 712 (e.g., a mouse, a keyboard, etc.); and one or more output devices 716 (e.g., a display device, a printer, etc.). The computer system 700 may also include one or more storage devices 720. By way of example, storage device(s) 720 may be disk drives, optical storage devices, solid-state storage devices such as a random access memory (“RAM”) and/or a read-only memory (“ROM”), which can be programmable, flash-updateable and/or the like.

The computer system 700 may additionally include a computer-readable storage media reader 724; a communications system 728 (e.g., a modem, a network card (wireless or wired), an infra-red communication device, etc.); and working memory 736, which may include RAM and ROM devices as described above. The computer system 700 may also include a processing acceleration unit 732, which can include a DSP, a special-purpose processor, and/or the like.

The computer-readable storage media reader 724 can further be connected to a computer-readable storage medium, together (and, optionally, in combination with storage device(s) 720) comprehensively representing remote, local, fixed, and/or removable storage devices plus storage media for temporarily and/or more permanently containing computer-readable information. The communications system 728 may permit data to be exchanged with a network and/or any other computer described above with respect to the computer environments described herein. Moreover, as disclosed herein, the term “storage medium” may represent one or more devices for storing data, including read only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and/or other machine readable mediums for storing information.

The computer system 700 may also comprise software elements, shown as being currently located within a working memory 736, including an operating system 740 and/or other code 744. It should be appreciated that alternate embodiments of a computer system 700 may have numerous variations from that described above. For example, customized hardware might also be used and/or particular elements might be implemented in hardware, software (including portable software, such as applets), or both. Further, connection to other computing devices such as network input/output devices may be employed.

Examples of the processors 340, 708 as described herein may include, but are not limited to, at least one of Qualcomm® Snapdragon® 800 and 801, Qualcomm® Snapdragon® 620 and 615 with 4G LTE Integration and 64-bit computing, Apple® A7 processor with 64-bit architecture, Apple® M7 motion coprocessors, Samsung® Exynos® series, the Intel® Core™ family of processors, the Intel® Xeon® family of processors, the Intel® Atom™ family of processors, the Intel Itanium® family of processors, Intel® Core® i5-4670K and i7-4770K 22 nm Haswell, Intel® Core® i5-3570K 22 nm Ivy Bridge, the AMD® FX™ family of processors, AMD® FX-4300, FX-6300, and FX-8350 32 nm Vishera, AMD® Kaveri processors, Texas Instruments® Jacinto C6000™ automotive infotainment processors, Texas Instruments® OMAP™ automotive-grade mobile processors, ARM® Cortex™-M processors, ARM® Cortex-A and ARM926EJ-S™ processors, other industry-equivalent processors, and may perform computational functions using any known or future-developed standard, instruction set, libraries, and/or architecture.

An embodiment of a data structure 800 representing sensor measurements associated with an object may be as shown in FIG. 8A. The measurements 804, 808, 812, 816 represent one or more sensor measurements, such as those discussed above. Each measurement 804-816 can be associated with a time 820, 824, 828, 832. The time is from a present moment to some time in the past. As such, the measurements 804-816 represent a series of measurements of an object over a time period that is separated into portions 820, 824, 828, 832.

Each time portion 820, 824, 828, 832 may be at a predetermined interval, e.g., 5 microseconds, 20 milliseconds, etc. A most recent measurement 832 may be made at time 0. Each preceding measurement 804, 808, 812 may be made at time 0 minus some number of time portions (e.g., 10 time portions before, 5 time portions before, etc.). For example, time measurement 808 may be time 0 minus Y, where Y represent 19 time portions (19 times 5 microseconds or 95 microseconds) in the past.

There can be any number of time portions 820, 824, 828, 832 that result in any number of measurements 804, 808, 812, 816, as represented by ellipses 826. As such, the number of stored measurements 804, 808, 812, 816 can be predetermined or may be set by a user based on the accuracy of the measurement results desired. Further, the time portions 820, 824, 828, 832 can also be predetermined or user established also based on the accuracy of the measurement desired or the speed at which the sensors or processor can functionally obtain or analyze the measurements.

Each object around the vehicle 100 can have a set of measurements while the object is within physical proximity of the vehicle (e.g., 100 yards, 100 feet, etc.) based on whether the location of the object may affect the path of the vehicle 100. Thus, objects in front of the vehicle 100 or ahead of the direction of travel of the vehicle 100 may be measured more often or be measured more accurately than those objects behind the vehicle 100. As any number of objects may be within physical proximity of the vehicle 100, there may be more than a single data structure 800 stored by the vehicle processor 708, as represented by ellipses 836.

An embodiment of another data structure 838 representing constraints on how the vehicle 100 may maneuver or on how an object may behave may be as shown in FIG. 8B. In a first configuration, the constraints 840, 844, 848, 852 can represent one or more constraints on a vehicle 100. A constraint can be characteristics of the driving ability of the vehicle 100. For example, a constraint 840, 844, 848, 852 can include one or more of, but is not limited to, a steering range, an acceleration limit, an acceleration limit at a specific speed, a braking limit, a gravity force rating for the tires, a skid limit, etc.

Any of the constraints can be added to or modified by the context of the situation in which the vehicle 100 is driving. For example, if the vehicle 100 is driving in snow or rain, the deceleration and steering limits can change. The steering ability or maneuverability may be different at higher speeds than lower speeds, etc. These characteristics 840, 844, 848, 852, thus, can be learned or modified over time based on machine learning or based on database updates that result from the experience of this vehicle 100 or similar vehicles, as provided by a manufacturer.

In an alternative or addition embodiment, the constraints 840, 844, 848, 852 can represent one or more constraints on an object. Thus, the constraints 840, 844, 848, 852 can be characteristics associated with the behavior of other objects around the vehicle 100 as determined through machine learning. For example, a constraint 840, 844, 848, 852 can include one or more of, but is not limited to: a likely steering ability of another vehicle, an acceleration limit for another vehicle, an acceleration limit at a specific speed for another vehicle, a braking limit for another vehicle, a gravity-force rating for the tires for another vehicle, a skid limit for another vehicle, a speed of a pedestrian, a speed of a bicycle, a behavior trait for another vehicle, a driving situation (based on street signs, traffic signals, lane markers, road barriers, construction zones, etc.), a rule, a law, and other constraints.

Any of the constraints can be added to or modified by the context of the situation in which the vehicle 100 is driving. Again, for example, if the vehicle 100 is driving in snow or rain, the deceleration and steering limits can change. The steering ability or maneuverability may be different at higher speeds than lower speeds, etc. These object characteristics 840, 844, 848, 852, thus, can be learned or modified over time based on machine learning that result from the experience of this vehicle 100 or another vehicle, as provided by a manufacturer.

There can be any number of the constraints 840, 844, 848, 852, as represented by ellipses 856. As such, the number of stored constraints 804, 808, 812, 816 can be predetermined or may be set by a user based on how accurately the object or vehicle 100 behavior is to be determined. Each object encountered by the vehicle 100 can have a set of constraints. Thus, there may be more data structures 838 than those shown in FIG. 8B, as represented by ellipses 836.

For collision avoidance, the likely path of another vehicle or object needs to be determined. To accomplish the determination of the path of the object, the processor 708 can, for example, apply stochastic modeling to determine the probabilistic path of an object. The stochastic process is a probability model used to describe phenomena that evolves over time and/or space. For another vehicle or object, a stochastic process is a time sequence representing the evolution of movement of the vehicle or object represented by a positing in three-dimensional (or two-dimensional) space, where the position change is subject to a random variation. The output of the stochastic model is a probability that the vehicle or object will be in a particular position at some time “t” in the future. In using the stochastic process, the movement of the vehicle or object to the next state or position depends, at least partially, on the current state and or past states, as measured by the sensors and described in conjunction with FIG. 8A. Further, the stochastic process measuring the further position of the vehicle or object can also be based, at least partially, on the constraints 838, described in conjunction with FIG. 8B. Stochastic modeling is well known and is not described further herein.

In addition or in the alternative, the probabilistic vectors can be based on a machine learning model (e.g., deep learning) that can generate a probability distribution of the pedestrians, other cars, and other moving objects based on the context of the environment, the situation, and what other objects may do. For example, a if a walk sign is illuminated a and there is a car suddenly turning in front of the pedestrian, the pedestrian is more likely to disregard the sign and stop crossing an intersection. The paths of the rest of objects cars can be generated according to a constantly-improving machine learning model (e.g., a supervised learning/reinforcement learning).

A simple representation of the output 900 of the stochastic model for a given time “t” representing many possible positions and the probabilities that the vehicle or object will occupy one of those positions, may be as shown in FIGS. 9A and 9B. The ellipse 928, 936 represent a position in an X-Y coordinate space 904 (represented by axis 930 and 926, respectively). The ellipses 928, may provide all the possible positions for the vehicle or object based on the past measurements 600 and/or constraints 638. Thus, the ellipses 928 can represent a further position at time “t” where the vehicle or object is likely to move to based on the movement of the vehicle or object and the constraints to that movement.

A third axis (the “Z” axis) 908 can represent the probability that the vehicle or object will occupy that position. The probabilities can range from 100% to 0%. As shown in FIGS. 9A and 9B, the darker shaded areas are a higher probability than a lesser shaded area. Thus, the probabilistic representation of an object position at a time “t” in the future may be represented by the stochastic model. Another object, with a different trajectory, may have a different stochastic representation 936, as shown in the model 932 of FIG. 9B. Three representations 928, 936 and 1004, from three objects, may be as shown in combined model 1000 of FIG. 10. As such, the different stochastic models can be combined for different objects to provide an environmental profile at a time “t”, as shown in FIG. 10.

A simplified version of an output from a model for finding a probabilistic trajectory of avoiding an object in an environment 1100 may be as shown in FIG. 11. The probabilities of collision may be based on the stochastic model(s) describing the objects' vectors, possibly as shown in FIG. 10.

The model provides a series of vectors 1104 as shown in FIG. 11. Each vector 1104 may have a probability of a resultant collision with one or more of the objects in the driving environment. Thus, if a vehicle 100 proceeds along the vector 1104 or attempts to change to a different pathway, the probability of a collision can be determined. For example, the vehicle hazard detection component 1212 can, at a time “t” in the future, determine one or more possible positions of the vehicle 100 in a driving environment 1100 (which could be, for example, an XY coordinate system).

This future, possible position of the vehicle 100 may then be checked against the different vectors of all objects in the environment provided by the stochastic model(s) 1000. If a vector associated with an object would have a high probability of being in the same position as the vehicle 100 at that time t, then that vector 1400 would lead to a high chance of collision. The CPU 708 can determine the probability of an object being in the same position as the vehicle 100 at that time t by multiplying the probability of the object being in the position by the probability of the vehicle 100 being in the position. Then, the CPU 708 can compare the probability of the object and the vehicle 100 occupying the same position to a threshold. The threshold can be predetermined or be set by a user, manufacture, dealer, etc.

If the probability is above the threshold, the current or analyzed vector for the vehicle should be avoided. For example, if the collision has a 30 percent or greater chance of occurring, the vehicle 100 then must try to avoid that collision. As show in FIG. 11, each of the different vectors 1104 can have a probability rating that may be measured by the concentric circles having associated probabilities. For example, vector 1104A and 1104D have a near 80 percent or greater chance of causing a collision. While vectors 1104B and 1104E have a less than 40 percent chance of causing a collision, and vector 1104C has a 40 percent chance of causing a collision. These vectors 1104 may cover numerous possible vectors and provide a continuous probabilistic model of which vectors have high probabilities of collision and which do not. As such, the vehicle 100 can then determine a best path of travel by determining the least probable path for collision.

In another example, the vectors 1104 can represent the best vectors or the longest vectors without a collision. For example, vector 1104D would be the longest path without a collision while vector 1104B would have the shortest path before a collision. This configuration also may be used for the Monte Carlo analysis or represent the output of a Monte Carlo analysis to provide the best pathway for the vehicle to take to avoid any possible collisions.

An embodiment of a software and/or hardware system that may be executed as part of the CPU 708 or may be embodied in an application specific integrated circuit (ASIC), a system-on-chip (SoC), field programmable gate array (FPGA), or other type of hardware device may be as shown in FIG. 12. The system 1200 can include one or more different components that can be in communication with the vehicle control system 348 as described previously. As such, the vehicle control system 348 can receive the information from the different components 1204, 1208, 1212 to control the vehicle 100 to avoid a collision. The different components can include one or more of, but are not limited to: an object direction engine 1204, a best path engine 1208, and/or a vehicle hazard detection 1212.

The object direction engine 1204 can be the processing component that creates and output results from the stochastic model as explained previously. Thus, the object direction engine 1204 can receive data from the one or more sensors 304 and provide a determination of the probability vectors for the different objects, as explained previously in conjunction with FIGS. 9A through 10. This object information and the algorithms used therein may produce an output that is provided to the best path engine 1208.

The best path engine 1208 can process the Monte Carlo analysis to determine both the probability of a collision in any one position at any time “T” in the future and provides the longest path, for the vehicle 100, without having a collision. The best path engine 1208 can receive both the object information 1204 and the information in the constraints 638 to determine which path the vehicle 100 can or may move to best avoid any possible collisions. This information may be provided to the vehicle control system 348 to use in controlling the vehicle to maneuver along the best path.

In other configurations, this information may be passed to the vehicle hazard detection component 1212 to determine if the path chosen has a possibility of collision or whether an alert or an interrupt should be sent to the vehicle control system 348 for action. The best path engine 1208 does not control necessarily the vehicle control system 348, but the vehicle hazard detection 1212 can apply a standard or benchmark to determine if a maneuver needs to be made. If a maneuver does need to be made, that information may be sent from the vehicle hazard detection 1212 to the vehicle control system 348 to change the function of the vehicle, such as steering around an object.

An embodiment of a tree structure 1300 that may be used by the best path engine 1208 for the Monte Carlo simulation/analysis may be as shown in FIG. 13. After the CPU 308 has determinized the predicted probability distribution for all other moving objects, the CPU 308 can conduct a Mont Carlo simulation on the other moving objects that can be executed on each step of the Mont Carlo Tree Search (MCTS). Thus, the object vectors can be included in the Mont Carlo Tree Search to simulate the interaction between the vehicle 100 and other cars and other objects. For example, at a four-way stop where two or more cars arrive around the same time, if the vehicle 100 stops to wait, the other car(s) is likely to drive forward. If the vehicle 100 drives forward, the other car(s) is likely to wait). The Monte Carlo simulation can look for a furthest separation between the vehicle 100 and the other objects at each node in the MCTS based on various situations. The series of nodes having such characteristics should result in the best path for the vehicle 100 to maneuver,

The tree structure 1300 may have a root node 1304 that can be associated with the current position of the vehicle 100. Under the root node 1304 may be a series of two or more possible positions for the vehicle 100 at some time T1, T2, T3, etc. in the future. For example, if the granularity of the time measurements into the future is 20 microseconds, the child nodes 1308 and 1312 can represent the possible paths that the vehicle can take within the 20 microseconds into the future. Each level 1332, 1336, 1340, 1344 of the tree structure 1300 can represent a different time in the future. The lower the level the further into the future the position determine is. Thus, the child nodes 1308, 1312, at node level 1336, are nearer to the present time than the great grandchild nodes 1324, 1328 at node level 1344.

Each node can represent a possible path the vehicle 100 can take. For example, Path A 1308 and Path B 1312 can represent the possible paths that the vehicle 100 may take at some time T1 in the future. There are only two child nodes shown under root node 1304; however, there may be more than two child nodes (or two nodes in any level 1336, 1340, 1344, etc. under the root node 1304) depending on the granularity of the position analysis done by the best path engine 1208. Thus, if the best path engine 1208 can monitor or can deduce between inch increments over a space of one foot of possible paths, the best path engine 1208 may create 12 different child nodes under root node 1304. There may be variability in the number of child nodes in each level of the tree structure as the number of possible paths may increase further into the future based on the constraints of the vehicle's ability to move as discussed in conjunction with FIG. 8B. There may be more or fewer nodes 1308-1328 in each level 1336-1344, as represented by ellipses 1348. Further, there may be mode or fewer levels 1336-1344 in the tree 1300, as represented by ellipses 1352.

To determine the best path, the best path engine 1208 can traverse the tree structure 1300 for some number of increments represented by time T and associated with the levels of the tree 1300. Thus, if the granularity of the information is based on a 20-microsecond or more interval, for example, the best path engine 1208 can have some series of nodes or levels in the tree hierarchy 1300 based on how far into the future the best path engine 1208 is analyzing. The best path engine 1208, for example. may analyze over a time interval t of 30 seconds where the tree structure 1300 may have levels representing each interval time T1, T2, T3. etc. (20 microsecond steps) within that 30 second span.

To determine the best path, the best path engine 1208 can traverse the tree 1300 by looking at which child node 1308-1328 at each tree level 1336-1344 has the lowest probability of a collision. For example, the best path engine 1208 can determine that Path A 1308 has a better probability of avoiding a collision with the different objects than Path B 1312. Thus, the best path engine 1208 can traverse through node 1308 and evaluate the grandchild nodes 1316 and 1320 under node 1308. Similar to the traversal to Path A 1308, Path A2 1320 can be determined to have the lowest probability of collision, and the best path engine 1208 may then traverse to that node 1320. Similarly, the best path engine 1208 can determine one of the two great grandchild nodes A2 i 1324, A2 ii 1328 that has the best probability of avoiding a collision. By this traversal, the best path engine 1208 can identify a path, using the Monte Carlo analysis, that traverses the entire tree until finding a leaf node with the lowest probability of collision, which represents the longest path for the vehicle 100 to take without a collision. This tree structure traversal may only occur if the current path of the vehicle 100 may result in a collision. As such, the best path engine 1208 is looking for the longest path to avoid the current collision. This traversal need not be done unless a collision warning is determined by the vehicle hazard detection 1212 or object direction engine 1204.

An embodiment of a method 1400 for determining a path to avoid a collision may be as show in FIG. 14. A general order for the steps of the method 1400 is shown in FIG. 14. Generally, the method 1400 starts with a start operation 1404 and ends with operation 1424. The method 1400 can include more or fewer steps or can arrange the order of the steps differently than those shown in FIG. 14. The method 1400 can be executed as a set of computer-executable instructions executed by a computer system or processor and encoded or stored on a computer readable medium. In other configurations, the method 1400 may be executed by a series of components, circuits, gates, etc. created in a hardware device, such as a System of Chip (SOC), Application Specific Integrated Circuit (ASIC), and/r a Field Programmable Gate Array (FPGA). Hereinafter, the method 1400 shall be explained with reference to the systems, components, circuits, modules, software, data structures, signalling processes, models, environments, vehicles, etc. described in conjunction with FIGS. 1-13 and 18.

In step 1408, the object direction engine 1204 can determine the trajectory of one or more objects surrounding the vehicle 100 in the vehicle's environment. For example, as shown in FIG. 18, the environment 1800 can include the current vehicle 1804 and one or more objects, for example, three other vehicles 1808, 1812, 1816. The vehicle 1804 can receive sensor data 304 over some period of time 600 that may help to determine the probabilistic trajectory vector for each of the vehicles 1808, 1812, 1816, as described in conjunction with FIGS. 9A through 10.

Thus, each of the vehicles 1808, 1812, 1816 can have a different probabilistic trajectory, such as models 928, 936, and 1004 shown in FIG. 10. The method 1500 for determining the trajectory for the objects may be as described in conjunction with FIG. 15. The object trajectory information may then be provided to a vehicle hazard detection component 1212 or a best path engine 1208.

In step 1412, the best path engine 1208 or vehicle hazard detection component 1212 can determine if there is a possibility of collision for the vehicle 100, 1804 driving along the vehicle's current trajectory. The method 1600 for determining whether a collision is possible for the current trajectory or one or more other trajectories may be as described in conjunction with FIG. 16.

From the provided collision information, the vehicle hazard detection component 1212 or the best path engine 1208 may determine whether a collision needs to be avoided, in step 1416. Thus, the best path engine 1208 or vehicle hazard detection 1212 can determine, based on a comparison between the probability (of the vehicle 100, 1800 and another object occupying the same position at some time in the future) with a threshold, if the current trajectory will result in a collision. The threshold may be a setting for probability that a collision is going to occur at some time tin the future. If the probability meets and/or crosses the threshold, then the method 1400 proceeds “YES” to find the best path to avoid collision in step 1420. However, if the threshold is not crossed, then the method 1400 can proceed “NO” back to step 1408. The benchmark or threshold may be some probability. For example, if there is a 30 percent or greater probability of having a collision at some time t in the future while traveling along the current trajectory, the best path engine 1208 or vehicle hazard detection 1212 can determine that such a trajectory will result in a collision, and a new path needs to be determined.

In step 1420, the best path engine 1208 can determine the best path to avoid the collision. This best path determination method 1700 may be as described in conjunction with FIG. 17.

An embodiment of a method 1500 for determining object trajectories may be as shown in FIG. 15. Generally, the method 1500 starts with a start operation 1504 and ends with operation 1524. The method 1500 can include more or fewer steps or can arrange the order of the steps differently than those shown in FIG. 15. The method 1500 can be executed as a set of computer-executable instructions executed by a computer system or processor and encoded or stored on a computer readable medium. In other configurations, the method 1500 may be executed by a series of components, circuits, gates, etc. created in a hardware device, such as a System of Chip (SOC), Application Specific Integrated Circuit (ASIC), and/r a Field Programmable Gate Array (FPGA). Hereinafter, the method 1500 shall be explained with reference to the systems, components, circuits, modules, software, data structures, signalling processes, models, environments, vehicles, etc. described in conjunction with FIGS. 1-14 and 18.

The object direction engine 1204 may identify one or more objects, in step 1508. The sensor data from the sensors 304 may be provided to the object direction engine 1204. This sensor data may provide an indication of one or more moving objects, for example, vehicles 1808, 1812, 1816. Further, the sensor data, from the sensors 304, may also indicate one or more static objects such as barriers, lane markers, street signs, potholes, etc. The object direction engine 1204 can identify each object and provide an object identifier 802 for each of the different objects. Each object may then have the sensor data 800 associated with the object identifier 802 to begin and continue to monitor the physical position of the object over a continuous timespan broken into portions 1820, 1824, 1828, and/or 1832. From the sensor data, the object direction engine 1204 can determine probabilistic trajectories of the objects.

In step 1512, the object direction engine 1204 can create a trajectory distribution as described in conjunction with FIG. 9A through FIG. 10. For moving objects in a three-dimensional space, the trajectory can be predicted simply by the laws of physics. For moving objects that are governed by laws or other rules (e.g., cars, pedestrians, bicycles, etc.) the distribution of trajectories can be predicted with a machine learning model applied to the sensor data, which includes recognizing the type of object, recognizing applicable road/lanes, traffic signs, etc. The machine learning model can develop a current context for the driving situation and apply any relevant constraints 838 to the context.

The trajectory distribution may be a series of possible positions for the object, with a probability associated therewith, as described in conjunction with FIGS. 9A to 10. The probabilities of the future position of the one or more objects may be governed by the one or more constraints 838, as provided in conjunction with FIG. 8B. These constraints 838 can determine the possibilities of a moveable object, for example, car 1808 moving in a certain direction based on the car's speed, physical properties, and past machine learning by the vehicle 100. The trajectory distribution may be provided from a stochastic model and provide an output similar to that as described with outputs 928, 936, 1004 in conjunction with FIGS. 9A through 10. This information may then be provided to the vehicle hazard detection component 1212, in step 1516.

The vehicle hazard detection component 1212 can then determine, based on the vehicle's current trajectory, whether any of the object's probabilistic trajectories could cause a possible collision, in step 1516. With the above stochastic models associated with the objects, the vehicle hazard detection component 1212 can apply a function “f” to calculate and/or estimate the probability the vehicle 100, 1804 will collide with one or more of the other objects at time step t given the vehicle's position as P_x, P_y, where P_x is the probability of the vehicle 100, 1804 being at a position on an X axis and where P_y is the probability of the vehicle 100, 1804 being at a position on an Y axis. In some configurations, the vehicle hazard detection component 1212 can check a distance between the center of the vehicle 100, 1804 and the object to determine if a collision is possible. The function f(collide, P_x, P_y, t) may return either 0 (no collision) or 1 (collision) for deterministic trajectory; that is, the object and the vehicle 100, 1804 will occupy the same X,Y position at time t.

For moving objects with stochastic trajectories, the vehicle hazard detection component 1212 may discretize the trajectory distribution and record the object trajectories with an associated probability in a tree structure 1300. The vehicle hazard detection component 1212 can then check against all the possible trajectories of all objects to estimate the probability of collision for each step in time t.

Then, the vehicle hazard detection component 1212 can execute a Monte Carlo simulation based on the trajectory distribution of objects using the Monte Carlo tree 1300. The navigation system 302 can provide the vehicle's current trajectory. Given the driving trajectory, the probability for the collision can be calculated with the following algorithm:

Function (path_collision_probability(trajectory)) p = 1.0 For t (t=time step into the future) = 1 to T (T=total time span analyzed) (P_x, P_y) = get_position_from_trajectory(trajectory, t) p = p * (1 − f(collide, P_x, P_y, t)) endfor collision_probability = 1 − p return collision_probability

Thus, for each time “t” in the future, the vehicle hazard detection component 1212 can compare the returned collision probability from the algorithm above with a threshold (e.g., 50%). For example, if vehicle 1808 and vehicle 1804 are determined to have a high probability (e.g., 75%) of being in the same position at some time in the future, such as 10 seconds in the future, which is greater than the threshold, then there is a high possibility of a collision. This information from the vehicle hazard detection component 1212 can cause an alert to be created and passed to the best path engine 1208 to determine a solution for avoiding the collision.

In step 1520, the object direction engine 1204 can determine if there is another object to analyze. For example, if the object direction engine 1204 determines that a new vehicle, for example, vehicle 1812, is now within the environment 1800 of vehicle 1804, the object direction engine 1204 can identify that new car 1812 and conduct the process 1500 again. However, if there is no new object, the method 1500 may proceed “NO” to end operation 1524. If there is a new object or if the current objects need to be continually evaluated, as some objects remain in the environment 1800, the method 1500 may continue “YES” back to step 1508.

An embodiment of a method 1600 which may be conducted by the vehicle hazard detection component 1212 for determining the probability of a future collision and generating the tree structure 1300 may be as shown in FIG. 16. Generally, the method 1600 starts with a start operation 1604 and ends with operation 1624. The method 1600 can include more or fewer steps or can arrange the order of the steps differently than those shown in FIG. 16. The method 1600 can be executed as a set of computer-executable instructions executed by a computer system or processor and encoded or stored on a computer readable medium. In other configurations, the method 1600 may be executed by a series of components, circuits, gates, etc. created in a hardware device, such as a System of Chip (SOC), Application Specific Integrated Circuit (ASIC), and/r a Field Programmable Gate Array (FPGA). Hereinafter, the method 1600 shall be explained with reference to the systems, components, circuits, modules, software, data structures, signalling processes, models, environments, vehicles, etc. described in conjunction with FIGS. 1-15 and 18.

As explained above, the vehicle hazard detection component 1212 can set the analysis for some position X,Y, in step 1608. This X,Y position may be some position ahead of the vehicle 100, 1804 that may be reached at time t. This X,Y position can be in three dimensions for some situations, but, in this explanation, may be represented in an X-Y coordinate system of representing the distances or positions around the vehicle, for example, vehicle 1804.

The vehicle hazard detection component 1212 can then set the analysis for a time in the future, such as time step t, in step 1612. This time t can be some interval into the future over a predetermined time period T. For example, the time t can be two seconds into the future over a 30-second measurement or determination interval T. With the setting of the position 1608 and time t 1612, the hazard detection component 1212 may then determine if there is a possibility of collision at that position and at that time and what probability there is of that collision, using the algorithm described above.

In step 1616, the vehicle hazard detection component 1212 can determine the probability of whether an object and the vehicle 100 may occupy the same position at that time t. Thus, using the stochastic models 928, 936, 1004, the vehicle hazard detection component 1212 can determine if the current vector for the vehicle 100 may put the vehicle at the same position, as set in step 1608, at the time t, set in step 1612, as another object. As there is only a probability that an object will be at that position, the vehicle hazard detection 1212 can determine what the probability is for that position and time.

It may then set that probability for collusion for a node, as explained in FIG. 13, in step 1620. As such, the vehicle hazard detection component 1212 can create the tree 1300 as described in FIG. 13. In other words, the vehicle hazard detection component 1212 may generate each node 1308-1328, representing the positions, at different levels 1332-1344, representing the time intervals or steps t. The nodes, arranged in the tree 1300, can then be evaluated for collision probability (based on a comparison to a threshold as explained above) or a best path determination.

An embodiment of a method 1700 for determining a best path to avoid a collision as predicted by the vehicle hazard detection component 1212 may be as show in FIG. 17. Generally, the method 1700 starts with a start operation 1704 and ends with operation 1724. The method 1700 can include more or fewer steps or can arrange the order of the steps differently than those shown in FIG. 17. The method 1700 can be executed as a set of computer-executable instructions executed by a computer system or processor and encoded or stored on a computer readable medium. In other configurations, the method 1700 may be executed by a series of components, circuits, gates, etc. created in a hardware device, such as a System of Chip (SOC), Application Specific Integrated Circuit (ASIC), and/r a Field Programmable Gate Array (FPGA). Hereinafter, the method 1700 shall be explained with reference to the systems, components, circuits, modules, software, data structures, signalling processes, models, environments, vehicles, etc. described in conjunction with FIGS. 1-16 and 18.

To find the path to minimize the collision probability, the best path engine 1208 can execute the process 1700 based on a Monte Carlo Tree Search algorithm. The best path engine 1208 can first select a node within the tree 1300 in step 1708. Using a Monte Carlo analysis for the root tree 1300, the best path engine 1208 can traverse the tree for searching for the best path. Each node within the path can be constrained by the constraints 838. As such, some of the tree may be ignored as not being accessible to the vehicle based on the vehicle's constraints or abilities as described in conjunction with FIG. 8B.

After beginning at the root note 1304, the best path engine 1208 can calculate the weight of each child node 1308-1328, in step 1712. The weight of the child can be equivalent to which node can be accessed the easiest and a probability of any collision at that node. Based on the weight, the best path engine 1208 can continue to traverse the tree down the child nodes 1316, 1320, 1324, and 1328. For each of the child nodes 1308-1328, a weight can be calculated. For example, the weight of each child node may calculated as: Weight(child)=−Log(mean_collision_probability_for_all_visited_trajectories)*(1/minimum_collision_probability_among_all_visited_trajectories)

The child node can be randomly selected for expansion with the probability of escaping a collision proportional to the weight of the child. Thus, each probability associated with a node 1308-1328 can be multiplied by the weight to normalize the probabilities in the tree 1300.

After applying the weights to the possible child nodes 1308-1328, the best path engine 1208 can determine if the level of nodes 1332-1344 being evaluated are leaf nodes, in step 1716. If the level is not a leaf node (i.e., the node has children), the method 1700 can proceed “NO” back to step 1708 to select another node and calculate the weight of the children, in step 1712. If the nodes currently being evaluated are leaf nodes, the method 1700 may proceed “YES” to determine the best leaf node, in step 1720. Evaluating the leaf node of Monte Carlo Search can entail estimating the leaf node collision probability based on the machine learning models. For example, the algorithm may search for the best average distance between the vehicle and the closest object as the evaluation function.

This best path will have the lowest possibility or probability of any collision within the path of nodes from the root node 1304 to the leaf node, determined through traversal of the tree 1300. Thus, this best path engine 1208 can determine which is the best child node of the root after doing the tree traversal and can send this information to the vehicle control system 348 to avoid any accident that may be detected by the vehicle hazard detection component 1212 by taking the best path.

Any of the steps, functions, and operations discussed herein can be performed continuously and automatically.

The exemplary systems and methods of this disclosure have been described in relation to vehicle systems and electric vehicles. However, to avoid unnecessarily obscuring the present disclosure, the preceding description omits a number of known structures and devices. This omission is not to be construed as a limitation of the scope of the claimed disclosure. Specific details are set forth to provide an understanding of the present disclosure. It should, however, be appreciated that the present disclosure may be practiced in a variety of ways beyond the specific detail set forth herein.

Furthermore, while the exemplary embodiments illustrated herein show the various components of the system collocated, certain components of the system can be located remotely, at distant portions of a distributed network, such as a LAN and/or the Internet, or within a dedicated system. Thus, it should be appreciated, that the components of the system can be combined into one or more devices, such as a server, communication device, or collocated on a particular node of a distributed network, such as an analog and/or digital telecommunications network, a packet-switched network, or a circuit-switched network. It will be appreciated from the preceding description, and for reasons of computational efficiency, that the components of the system can be arranged at any location within a distributed network of components without affecting the operation of the system.

Furthermore, it should be appreciated that the various links connecting the elements can be wired or wireless links, or any combination thereof, or any other known or later developed element(s) that is capable of supplying and/or communicating data to and from the connected elements. These wired or wireless links can also be secure links and may be capable of communicating encrypted information. Transmission media used as links, for example, can be any suitable carrier for electrical signals, including coaxial cables, copper wire, and fiber optics, and may take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.

While the flowcharts have been discussed and illustrated in relation to a particular sequence of events, it should be appreciated that changes, additions, and omissions to this sequence can occur without materially affecting the operation of the disclosed embodiments, configuration, and aspects.

A number of variations and modifications of the disclosure can be used. It would be possible to provide for some features of the disclosure without providing others.

In yet another embodiment, the systems and methods of this disclosure can be implemented in conjunction with a special purpose computer, a programmed microprocessor or microcontroller and peripheral integrated circuit element(s), an ASIC or other integrated circuit, a digital signal processor, a hard-wired electronic or logic circuit such as discrete element circuit, a programmable logic device or gate array such as PLD, PLA, FPGA, PAL, special purpose computer, any comparable means, or the like. In general, any device(s) or means capable of implementing the methodology illustrated herein can be used to implement the various aspects of this disclosure. Exemplary hardware that can be used for the present disclosure includes computers, handheld devices, telephones (e.g., cellular, Internet enabled, digital, analog, hybrids, and others), and other hardware known in the art. Some of these devices include processors (e.g., a single or multiple microprocessors), memory, nonvolatile storage, input devices, and output devices. Furthermore, alternative software implementations including, but not limited to, distributed processing or component/object distributed processing, parallel processing, or virtual machine processing can also be constructed to implement the methods described herein.

In yet another embodiment, the disclosed methods may be readily implemented in conjunction with software using object or object-oriented software development environments that provide portable source code that can be used on a variety of computer or workstation platforms. Alternatively, the disclosed system may be implemented partially or fully in hardware using standard logic circuits or VLSI design. Whether software or hardware is used to implement the systems in accordance with this disclosure is dependent on the speed and/or efficiency requirements of the system, the particular function, and the particular software or hardware systems or microprocessor or microcomputer systems being utilized.

In yet another embodiment, the disclosed methods may be partially implemented in software that can be stored on a storage medium, executed on programmed general-purpose computer with the cooperation of a controller and memory, a special purpose computer, a microprocessor, or the like. In these instances, the systems and methods of this disclosure can be implemented as a program embedded on a personal computer such as an applet, JAVA® or CGI script, as a resource residing on a server or computer workstation, as a routine embedded in a dedicated measurement system, system component, or the like. The system can also be implemented by physically incorporating the system and/or method into a software and/or hardware system.

Although the present disclosure describes components and functions implemented in the embodiments with reference to particular standards and protocols, the disclosure is not limited to such standards and protocols. Other similar standards and protocols not mentioned herein are in existence and are considered to be included in the present disclosure. Moreover, the standards and protocols mentioned herein and other similar standards and protocols not mentioned herein are periodically superseded by faster or more effective equivalents having essentially the same functions. Such replacement standards and protocols having the same functions are considered equivalents included in the present disclosure.

The present disclosure, in various embodiments, configurations, and aspects, includes components, methods, processes, systems and/or apparatus substantially as depicted and described herein, including various embodiments, subcombinations, and subsets thereof. Those of skill in the art will understand how to make and use the systems and methods disclosed herein after understanding the present disclosure. The present disclosure, in various embodiments, configurations, and aspects, includes providing devices and processes in the absence of items not depicted and/or described herein or in various embodiments, configurations, or aspects hereof, including in the absence of such items as may have been used in previous devices or processes, e.g., for improving performance, achieving ease, and/or reducing cost of implementation.

The foregoing discussion of the disclosure has been presented for purposes of illustration and description. The foregoing is not intended to limit the disclosure to the form or forms disclosed herein. In the foregoing Detailed Description for example, various features of the disclosure are grouped together in one or more embodiments, configurations, or aspects for the purpose of streamlining the disclosure. The features of the embodiments, configurations, or aspects of the disclosure may be combined in alternate embodiments, configurations, or aspects other than those discussed above. This method of disclosure is not to be interpreted as reflecting an intention that the claimed disclosure requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment, configuration, or aspect. Thus, the following claims are hereby incorporated into this Detailed Description, with each claim standing on its own as a separate preferred embodiment of the disclosure.

Moreover, though the description of the disclosure has included description of one or more embodiments, configurations, or aspects and certain variations and modifications, other variations, combinations, and modifications are within the scope of the disclosure, e.g., as may be within the skill and knowledge of those in the art, after understanding the present disclosure. It is intended to obtain rights, which include alternative embodiments, configurations, or aspects to the extent permitted, including alternate, interchangeable and/or equivalent structures, functions, ranges, or steps to those claimed, whether or not such alternate, interchangeable and/or equivalent structures, functions, ranges, or steps are disclosed herein, and without intending to publicly dedicate any patentable subject matter.

Any one or more of the aspects/embodiments as substantially disclosed herein.

Any one or more of the aspects/embodiments as substantially disclosed herein optionally in combination with any one or more other aspects/embodiments as substantially disclosed herein.

One or means adapted to perform any one or more of the above aspects/embodiments as substantially disclosed herein.

The phrases “at least one,” “one or more,” “or,” and “and/or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C,” “at least one of A, B, or C,” “one or more of A, B, and C,” “one or more of A, B, or C,” “A, B, and/or C,” and “A, B, or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together.

The term “a” or “an” entity refers to one or more of that entity. As such, the terms “a” (or “an”), “one or more,” and “at least one” can be used interchangeably herein. It is also to be noted that the terms “comprising,” “including,” and “having” can be used interchangeably.

The term “automatic” and variations thereof, as used herein, refers to any process or operation, which is typically continuous or semi-continuous, done without material human input when the process or operation is performed. However, a process or operation can be automatic, even though performance of the process or operation uses material or immaterial human input, if the input is received before performance of the process or operation. Human input is deemed to be material if such input influences how the process or operation will be performed. Human input that consents to the performance of the process or operation is not deemed to be “material.”

Aspects of the present disclosure may take the form of an embodiment that is entirely hardware, an embodiment that is entirely software (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module,” or “system.” Any combination of one or more computer-readable medium(s) may be utilized. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.

A computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer-readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer-readable signal medium may be any computer-readable medium that is not a computer-readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including, but not limited to, wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

The terms “determine,” “calculate,” “compute,” and variations thereof, as used herein, are used interchangeably and include any type of methodology, process, mathematical operation or technique.

The term “electric vehicle” (EV), also referred to herein as an electric drive vehicle, may use one or more electric motors or traction motors for propulsion. An electric vehicle may be powered through a collector system by electricity from off-vehicle sources, or may be self-contained with a battery or generator to convert fuel to electricity. An electric vehicle generally includes a rechargeable electricity storage system (RESS) (also called Full Electric Vehicles (FEV)). Power storage methods may include: chemical energy stored on the vehicle in on-board batteries (e.g., battery electric vehicle or BEV), on board kinetic energy storage (e.g., flywheels), and/or static energy (e.g., by on-board double-layer capacitors). Batteries, electric double-layer capacitors, and flywheel energy storage may be forms of rechargeable on-board electrical storage.

The term “hybrid electric vehicle” refers to a vehicle that may combine a conventional (usually fossil fuel-powered) powertrain with some form of electric propulsion. Most hybrid electric vehicles combine a conventional internal combustion engine (ICE) propulsion system with an electric propulsion system (hybrid vehicle drivetrain). In parallel hybrids, the ICE and the electric motor are both connected to the mechanical transmission and can simultaneously transmit power to drive the wheels, usually through a conventional transmission. In series hybrids, only the electric motor drives the drivetrain, and a smaller ICE works as a generator to power the electric motor or to recharge the batteries. Power-split hybrids combine series and parallel characteristics. A full hybrid, sometimes also called a strong hybrid, is a vehicle that can run on just the engine, just the batteries, or a combination of both. A mid hybrid is a vehicle that cannot be driven solely on its electric motor, because the electric motor does not have enough power to propel the vehicle on its own.

The term “rechargeable electric vehicle” or “REV” refers to a vehicle with on board rechargeable energy storage, including electric vehicles and hybrid electric vehicles. 

What is claimed is:
 1. A vehicle, comprising: a sensor to sense an environment surrounding the vehicle; a vehicle control system to autonomously control driving functions of the vehicle; a processor in communication with the sensor and the vehicle control system, the processor to: identify an object within the environment surrounding the vehicle; determine a probabilistic vector for the object; determine, based on the probabilistic vector for the object, if the object may collide with the vehicle; determine a best path to avoid the object from a tree comprising potential paths for the vehicle generated by the processor by: determining a root node for the tree, the root node associated with a current position of the vehicle at a first time; determining two or more child nodes depending from the root node in the tree and corresponding to two or more different possible positions for the vehicle at a second time subsequent to the first time; determining probabilities of collision between the object and the vehicle for the two or more child nodes; determining weights for the two or more child nodes; normalizing the two or more child nodes by multiplying a respective weight by a respective probability of collision for each of the two or more child nodes; determining, prior to the second time and based on the probabilistic vector for the object and the two or more different possible positions for the vehicle at the second time, a single child node of the two or more child nodes having a lowest probability of a collision between the object and the vehicle at the second time; determining two or more grandchild nodes depending from the single child node in the tree and corresponding to two or more different possible positions for the vehicle at a third time subsequent to the second time; determining, prior to the third time and based on the probabilistic vector for the object and the two or more different possible positions for the vehicle at the third time, a single grandchild node of the two or more grandchild nodes having a lowest probability of a collision between the object and the vehicle at the third time, wherein the best path to avoid the object is one of the potential paths linking the root node to the single grandchild node via the single child node in the tree; and provide the best path to the vehicle control system to control the vehicle along the best path and avoid collision with the object.
 2. The vehicle of claim 1, wherein determining the best path to avoid the object is further generated by the processor: determining, two or more great grandchild nodes depending from the single grandchild node in the tree and corresponding to two or more different possible positions for the vehicle at a fourth time subsequent to the third time; and determining, prior to the fourth time and based on the probabilistic vector for the object and the two or more different possible positions for the vehicle at the fourth time, a single great grandchild node of the two or more great grandchild nodes having a lowest probability of a collision between the object and the vehicle at the fourth time, and wherein the best path to avoid the object is one of the potential paths linking the root node to the single great grandchild node via the single child node and the single grandchild node in the tree.
 3. The vehicle of claim 1, wherein determining the best path to avoid the object comprises determining dependent nodes from the root node in the tree only from a corresponding parent node having a lowest probability of a collision at a corresponding time.
 4. The vehicle of claim 1, wherein the respective weight for each child node is equal to a negative logarithm of a mean collision probability for all visited trajectories of the vehicle multiplied by one divided by a minimum collision probability among all the visited trajectories of the vehicle.
 5. The vehicle of claim 1, wherein the probabilistic vector for the object is based on a stochastic model of possible vectors for the object, wherein the object is a static object or a moving object, wherein the object is another vehicle, and wherein the probabilistic vector for the object is modified by a constraint.
 6. The vehicle of claim 5, wherein the constraint is based on a law of physics, a rule, a law, a machine-learned behavior, and/or an ability of the object.
 7. The vehicle of claim 6, wherein determining if the object may collide with the vehicle is based on traversing a tree representing probabilities that the object and the vehicle will be at a same position at a future time.
 8. The vehicle of claim 7, wherein each level of the tree represents a different time step tin the future.
 9. The vehicle of claim 8, wherein a tree size is based on a total evaluation period T.
 10. The vehicle of claim 9, wherein each node represents a different position at the different time step t.
 11. The vehicle of claim 10, wherein a Monte Carlo Search is conducted on the tree to determine if there is a probability of a collision.
 12. The vehicle of claim 11, wherein the probability of the collision at a node is compared to a threshold to determine if there is going to be a collision and the processor needs to determine the best path.
 13. The vehicle of claim 11, wherein the Monte Carlo Search also searches for a leaf node in the tree that represents the best path having a lowest, weighted probability of a collision.
 14. The vehicle of claim 13, wherein a search function for the Monte Carlo Search is based on a furthest distance between the object and the vehicle at a leaf node.
 15. A method, comprising: sensing, by a sensor of a vehicle, sensor information relating to an environment surrounding the vehicle; sending, by the sensor, the sensor information to a processor; identifying, by the processor, an object within the environment surrounding the vehicle based on the sensor information; determining, by the processor, a probabilistic vector for the object based on a stochastic model; determining, by the processor based on the probabilistic vector for the object, if the object may collide with the vehicle; determining, by the processor, a best path to avoid the object from a tree comprising potential paths for the vehicle generated by the processor determining a root node for the tree, the root node being associated with a current position of the vehicle at a first time, wherein determining the best path includes: determining two or more child nodes depending from the root node in the tree and corresponding to two or more different possible positions for the vehicle at a second time subsequent to the first time, determining probabilities of collision between the object and the vehicle for the two or more child nodes, determining weights for the two or more child nodes, normalizing the two or more child nodes by multiplying a respective weight by a respective probability of collision for each of the two or more child nodes, determining, prior to the second time and based on the probabilistic vector for the object and the two or more different possible positions for the vehicle at the second time, a single child node of the two or more child nodes having a lowest probability of a collision between the object and the vehicle at the second time, determining two or more grandchild nodes depending from the single child node in the tree and corresponding to two or more different possible positions for the vehicle at a third time subsequent to the second time, and determining, prior to the third time and based on the probabilistic vector for the object and the two or more different possible positions for the vehicle at the third time, a single grandchild node of the two or more grandchild nodes having a lowest probability of a collision between the object and the vehicle at the third time, and wherein the best path to avoid the object is one of the potential paths linking the root node to the single grandchild node via the single child node in the tree; providing, by the processor, the best path to a vehicle control system; and controlling, autonomously by the vehicle control system, driving functions of the vehicle to maneuver the vehicle along the best path and avoid collision with the object.
 16. The method of claim 15, wherein the determining, by the processor, the best path to avoid the object further comprises determining two or more great grandchild nodes depending from the single grandchild node in the tree and corresponding to two or more different possible positions for the vehicle at a fourth time subsequent to the third time; and determining, prior to the fourth time and based on the probabilistic vector for the object and the two or more different possible positions for the vehicle at the fourth time, a single great grandchild node of the two or more great grandchild nodes having a lowest probability of a collision between the object and the vehicle at the fourth time, wherein the best path to avoid the object is one of the potential paths linking the root node to the single great grandchild node via the single child node and the single grandchild node in the tree.
 17. The method of claim 16, wherein determining, by the processor, the best path to avoid the object comprises determining dependent nodes from the root node in the tree only from a corresponding parent node having a lowest probability of a collision at a corresponding time.
 18. The method of claim 17, wherein the probabilistic vector for the object is modified by a constraint, wherein the constraint is based on a law of physics, a rule, a law, a machine-learned behavior, and/or an ability of the object, wherein determining if the object may collide with the vehicle is based on traversing the tree representing probabilities that the object and the vehicle will be at a same position at a future time, wherein each level of the tree represents a different time step t in the future, wherein a tree size is based on a total evaluation period T, wherein each node represents a different position at the different time step t, wherein a Monte Carlo Search is conducted on the tree to determine if there is a probability of a collision, wherein the probability of the collision at a node is compared to a threshold to determine if there is going to be a collision and the processor needs to determine the best path, wherein the Monte Carlo Search also searches for a leaf node in the tree that represents the best path having a lowest, weighted probability of the collision.
 19. A non-transitory information storage media having stored thereon one or more instructions, that when executed by one or more processors, cause a vehicle to perform a method, the method comprising: sensing an environment surrounding the vehicle to obtain sensor information; identifying an object within the environment based on the sensor information; determining a probabilistic vector for the object based on a stochastic model, wherein the probabilistic vector for the object is modified by a constraint, wherein the constraint is based on a law of physics, a rule, a law, a machine-learned behavior, and/or an ability of the object; determining, based on the probabilistic vector for the object, if the object may collide with the vehicle; generating, in response to determining that the object may collide with the vehicle, a tree comprising potential paths for the vehicle to follow relative to the object, wherein the generating the tree comprises: determining a root node for the tree, the root node being associated with a current position of the vehicle at a first time; determining two or more child nodes depending from the root node in the tree and corresponding to two or more different possible positions for the vehicle at a second time subsequent to the first time; determining probabilities of collision between the object and the vehicle for the two or more child nodes; determining weights for the two or more child nodes; normalizing the two or more child nodes by multiplying a respective weight by a respective probability of collision for each of the two or more child nodes; determining, prior to the second time and based on the probabilistic vector for the object and the two or more different possible positions for the vehicle at the second time, a single child node of the two or more child nodes having a lowest probability of a collision between the object and the vehicle at the second time; determining two or more grandchild nodes depending from the single child node in the tree and corresponding to two or more different possible positions for the vehicle at a third time subsequent to the second time; and determining, prior to the third time and based on the probabilistic vector for the object and the two or more different possible positions for the vehicle at the third time, a single grandchild node of the two or more grandchild nodes having a lowest probability of a collision between the object and the vehicle at the third time; determining a best path for the vehicle to follow to avoid the object by traversing the tree, wherein traversing the tree comprises evaluating dependent nodes from the root node in the tree only from a corresponding parent node having a lowest probability of a collision at a corresponding time, and wherein the best path is a path from the root node to the single grandchild node via the single child node in the tree; providing the best path to a vehicle control system; and controlling, autonomously by the vehicle control system, driving functions of the vehicle to maneuver the vehicle along the best path and avoid collision with the object.
 20. The non-transitory information storage media of claim 19, wherein a Monte Carlo Search is conducted on the tree to determine if there is a probability of a collision, and wherein a search function for the Monte Carlo Search is based on a furthest distance between the object and the vehicle at a leaf node. 